Impaired autophagic flux in the human brain after traumatic brain injury

Abstract

Emerging evidence indicates that dysfunctional autophagic flux significantly contributes to the pathology of experimental traumatic brain injury (TBI). The current study aims to clarify its role post-TBI using brain tissues from TBI patients. Histological examinations, including hematoxylin and eosin, Nissl staining, and brain water content analysis, were employed to monitor brain damage progression. Electron microscopy was used to visualize autophagic vesicles. Western blotting and immunohistochemistry were performed to analyze the levels of important autophagic flux-related proteins such as Beclin1, autophagy-related protein 5, lipidated microtubule-associated protein light-chain 3 (LC3-II), autophagic substrate sequestosome 1 (SQSTM1/p62), and cathepsin D (CTSD), a lysosomal enzyme. Immunofluorescence assays evaluated LC3 colocalization with NeuN, P62, or CTSD, and correlation analysis linked autophagy-related protein levels with brain water content and Nissl bodies. Early-stage TBI results showed increased autophagic vesicles and LC3-positive neurons, suggesting autophagosome accumulation due to enhanced initiation and reduced clearance. As TBI progressed, LC3-II and P62 levels increased, while CTSD levels decreased. This indicates autophagosome overload from impaired degradation rather than increased initiation. The study reveals a potential association between worsening brain damage and impaired autophagic flux post-TBI, positioning improved autophagic flux as a viable therapeutic target for TBI.

Introduction

Traumatic brain injury (TBI) remains a prominent reason for long-term disability and mortality worldwide, particularly among individuals less than 45 years of age [1]. Every year, more than 50 million individuals suffer from TBI worldwide [2]. Although extensive efforts have been made to explore effective strategies for TBI, prognoses remain poor owing to the lack of effective interventions [3,4]. Therefore, developing new effective strategies to improve TBI prognosis is urgently warranted. Based on its pathological process, TBI can be classified into two stages: the primary injury stage, which is caused by an external mechanical force, and the secondary injury stage, which is exacerbated by a series of pathophysiological cascades. Primary injury immediately occurs after the primary impact, resulting in irreversible brain damage. On the other hand, secondary brain injury, which occurs because of delayed neurochemical, metabolic, and cellular responses, can develop over hours to days after the initial traumatic insult and is the key focus of medical intervention for patients with TBI [5]. Brain edema, blood–brain barrier damage, excitotoxicity, oxidative stress, neuroinflammation, mitochondrial dysfunction, osmotic imbalance, metabolic dysfunction, and programmed cell death encompass the underlying mechanisms of secondary injury, ultimately leading to neurological dysfunctions [6,7].

As a catabolic process in cells, autophagy eliminates damaged organelles and toxic protein aggregates via the orderly degradation and recycling of cellular components [8]. It plays a vital role in maintaining normal physiological responses. Recently, several studies have revealed that aberrant autophagy enhances many pathophysiological processes, including aging, metabolic disorders, cancer, neurodegenerative diseases, infectious diseases, and TBI [9–12]. In both in vivo and in vitro TBI models, abnormal changes were observed in autophagy marker indices, with dysregulated autophagy being closely associated with unfavorable neurological dysfunction after experimental TBI [13,14]. Therefore, regulating autophagy holds promise as a potential therapeutic target for TBI. Autophagy comprises several dynamic phases, and studies are increasingly recommending the comprehensive evaluation of cargo movement throughout the autophagy system, called autophagic flux, to accurately and extensively determine the state of autophagy [15–18]. However, whether autophagic flux is impaired in patients with TBI remains unclear. Therefore, in the present study, we assessed the protein levels of key autophagy-related markers—Beclin1, Atg5, lipidated microtubule-associated protein light-chain 3 (LC3), autophagic substrate sequestosome 1 (SQSTM1/P62), and cathepsin D (CTSD), a lysosomal protease—in human brain tissues after TBI to investigate the status of autophagic flux.

Materials and methods

Clinical specimens

Twenty-eight patients with TBI who underwent emergency craniotomy to remove intracranial hematomas and contusion foci at the Second Hospital of Hebei Medical University were included in this study. After removing easily aspirated necrotic cores during surgery, the peri-ischemic zone (PIZ) becomes distinguishable from adjacent white matter regions macroscopically. Tissue samples, not exceeding 3mm³, are obtained using tumor forceps from the PIZ. The selection of biopsy sites is documented by the surgeon, aiming to align with preoperative CT findings, typically situated in the frontal, temporal, or parietal lobes [19]. The inclusion criteria were as follows: (1) adults (18–70 years old) and (2) surgical indications, including progressive disturbance of consciousness and neurological dysfunction, uncontrolled intracranial hypertension, noticeable mass effect with a midline shift of >5 mm, or intracranial hematoma with a volume of >20 ml. The exclusion criteria were as follows: patients with open craniocerebral injuries, secondary surgeries, or previous central nervous system diseases. The control group comprised five patients who underwent resection of small meningiomas in the trigone of the lateral ventricle. Brain specimens were obtained from control patients via precuneus corticotomy using the contralateral interhemispheric transfalcine transprecuneus approach. Brain tissues were immediately submerged in liquid nitrogen and subsequently stored at −80 °C for biochemical analysis. For histological examination, brain samples were fixed with 4% buffered paraformaldehyde and paraffin-embedded after conventional dehydration, followed by the preparation of 5-µm thick slices. Figure 1a and b presents the characteristic imaging data of the patients. Table 1 summarizes the primary clinical data. Before undergoing craniotomy, all patients (or their immediate family members) provided informed consent. The Ethics Committee of the Second Hospital of Hebei Medical University approved this study (approval number: 2023-R096), which was conducted according to the Declaration of Helsinki.

Fig. 1.

Representative imaging data and changes in brain histopathology, neuronal injury, and cerebral edema after TBI. (a) Preoperative MRI revealing a small meningioma in the trigone region of the lateral ventricle in one patient. The tumor was resected using precuneus corticotomy via the contralateral interhemispheric transfalcine transprecuneus approach. The arrow represents the surgical trajectory from which the contralateral precuneus brain tissue was obtained. (b) Preoperative computed tomography of a patient with a left temporal lobe brain contusion who underwent surgical resection of the brain contusion. The arrow represents the location from which the injured brain tissue was obtained. (c) HE staining showing the temporal pathological changes after TBI (scale bar = 100 µm). Arrows represent the vacuolar degeneration of neurons. (d) Representative images of Nissl-stained brain tissue sections (scale bar =100 µm or 50 µm). (e) Quantitative analysis revealing a gradual decrease in the IOD of the Nissl bodies in subgroups a, b, and c compared with the control group. (f) Changes in brain water content across different periods. *P < 0.05 compared with the control group; #P < 0.05 compared with the preceding group.

Table 1.

Clinical characteristics of the enrolled patients

No. of patient	Group	Age	Gender	Injury area	GCS scores	Time to injury (h)
Control
1	Control	38	Male	-	-	-
2	Control	66	Male	-	-	-
3	Control	33	Female	-	-	-
4	Control	20	Female	-	-	-
5	Control	53	Female	-	-	-
TBI
1	A	70	Male	L TL	10	5
2	A	47	Male	R TL	3	6
3	A	62	Male	L FL	3	6
4	A	31	Male	L TL	9	6
5	A	41	Male	R FL	11	6
6	A	51	Male	L FTL	12	6
7	B	53	Male	R FTL	3	8
8	B	69	Male	L TL	5	9
9	B	65	Female	L FTL	7	10
10	B	64	Male	R TL	6	13
11	B	55	Female	L FL	10	13
12	B	66	Male	R PL	4	14
13	B	67	Male	L TL	5	14
14	B	57	Male	R FTL	10	14
15	B	29	Male	R FTL	13	18
16	B	49	Male	L TL	6	20
17	B	49	Female	L FTL	11	24
18	C	68	Male	L FTL	3	25
19	C	50	Male	L TL	7	25
20	C	56	Male	R FTL	11	26
21	C	70	Male	R FTL	5	26
22	C	66	Male	R TL	6	28
23	C	58	Male	L FTL	5	29
24	C	49	Male	L TL	12	33
25	C	51	Female	L FTL	8	33
26	C	59	Male	L TL	10	54
27	C	41	Male	R FL	7	54
28	C	55	Male	L FL	12	122

FL, frontal lobe; FTL, frontal temporal lobe; L, left; PL, parietal lobe; R, right; TL, temporal lobe.

Hematoxylin and eosin staining

Hematoxylin and eosin (HE) staining was conducted according to a previously described method [20]. Briefly, after deparaffinizing and rehydrating 4-µm thick sections, the slides were stained with hematoxylin for 5 min, differentiated with 1% hydrochloric acid alcohol for 25 s, and counterstained with eosin for 5 min. Subsequently, the slides were gradually dehydrated using ascending alcohol concentrations and treated with dimethylbenzene. The images were observed under a light microscope (Zeiss, Germany).

Nissl staining

Nissl staining was conducted according to the manufacturer’s instructions. After routine deparaffinization and hydration, paraffin sections were incubated at 50 °C–60 °C and treated with Nissl’s staining solution (G1438, Solarbio, Beijing, China) for 30 min. Subsequently, three random 200× fields were chosen from the stained sections and captured under a light microscope (Zeiss). After preserving the original images, Image-Pro Plus 6.0 analysis software (Media Cybernetics, Bethesda, USA) was used to determine the integrated optical density (IOD) values of the Nissl bodies in each field.

Evaluation of brain edema

A previously described method was used to assess brain water content [20]. An electric analytical balance was used to determine the wet weight (WW) of tissue samples. Subsequently, they were desiccated at 100 °C for 24 h to obtain the dry weight (DW). The relative brain water content was calculated using the following formula:

Brain water content (%) = (WW − DW)/WW × 100%.

Electron microscopy

Brain tissue samples (1 × 1 × 1 mm) were sequentially fixed with 2.5% glutaraldehyde and 1% osmium tetroxide, dehydrated, and sectioned into 50–60 nm thick slices. Thereafter, the sections were observed and scanned under a transmission electron microscope (Hitachi, Japan).

Western blotting

As per a previously described method, protein lysis buffer (Solarbio) was used to extract tissue proteins [21]. The BCA Protein Assay kit (Solarbio) was used to measure the protein concentration in the samples. Subsequently, proteins were subjected to 12% SDS–PAGE, followed by transferring onto PVDF membranes (Merck Millipore). The membranes were blocked with 5% skim milk for 2 h. Then, the membranes were incubated with primary antibodies against Beclin1 (1:1000; MBL, Japan), Atg5 (1:1000; HUABIO, China), LC3 (1:1000; Genetex, USA), SQSTM1/P62 (1:1000; MBL), CTSD (1:1000; HUABIO), and GAPDH (1:1000; Affinity, China) overnight at 4 °C. Subsequently, the membranes were incubated with fluorescently labeled secondary antibodies [Anti-Rabbit IgG (H&L) Goat antibody Dy Light 800 Conjugated, 1:10 000, Abbkine] for 1 h at room temperature. The relative density of each band was assessed using the Odyssey infrared scanner (LICOR Bioscience, USA). ImageJ software was used to quantify the band densities.

Immunohistochemical analysis

A previously described method was used to conduct immunohistochemical staining, with slight modifications [22]. Serial sections (4 µm thick) were sliced and routinely deparaffinized. The sections were then exposed to 3% H₂O₂ for 30 min at room temperature and subsequently blocked with goat serum for 1 h at room temperature. Subsequently, the sections were incubated overnight at 4 °C with primary antibodies against Beclin1 (1:200; MBL), Atg5 (1:200; HUABIO), LC3 (1:200; Genetex), and SQSTM1/P62 (1:200; MBL). After washing with PBS, the sections were separately incubated with a biotinylated secondary antibody (1:200; ZSGB-BIO, China) and horseradish peroxidase–streptavidin (1:200; ZSGB-BIO) at room temperature for 30 min. 3, 3′-Diaminobenzidine was used to stain the cells. Three 200× fields were randomly selected from the stained sections and observed under a light microscope (Zeiss). Image analysis was conducted, and Image-Pro Plus 6.0 software was used to determine IOD values.

Immunofluorescence staining

Serial sections (4 µm thick) were sliced and deparaffinized. Then, the sections were washed with PBS, treated with 0.1% Triton X-100 for 30 min, and subsequently incubated with 10% goat serum for 1 h. Thereafter, the slices were incubated with primary antibodies against NeuN (1:200; Arigo, China), LC3B (1:200; Abclonal, China), CTSD (1:1000; HUABIO), and SQSTM1/P62 (1:200; MBL) overnight at 4 °C. The sections were additionally washed with PBS and then incubated with secondary antibodies [anti-rabbit IgG (H&L) goat antibody DyLight 488 conjugated and anti-mouse IgG (H&L) goat antibody DyLight 594 conjugated] for 1 h. Counterstaining was performed using 4′,6-diamidino-2-phenylindole for 10 min. The Axio Vert.A1 fluorescence microscope (Zeiss AG) was used to document the images. In each section, three randomly selected high-power fields (400× magnification) were selected. The mean count of positive cells across these fields was recorded as the section’s data. Two blinded pathologists performed all procedures.

Statistical analysis

SPSS 22.0 software (SPSS, Chicago, IL, USA) was used to analyze the experimental data. Count data were expressed as percentages, and the exact probability method was used to perform group comparisons. Measurement data were presented as mean ±SD. The two groups were compared using the Student’s t-test, whereas multiple groups were compared using one-way analysis of variance. The least significant difference method was used to conduct pairwise comparisons between groups. Correlation analysis was performed using either Pearson’s or Spearman’s correlation tests. A P-value of <0.05 was considered statistically significant.

Results

Demographic and clinical characteristics of patients

In this study, 33 patients (28 with TBI and 5 controls), were enrolled. The clinical characteristics of the enrolled patients are summarized in Table 1. Based on the duration of preoperative TBI, the injured brain tissues were divided into three subgroups: A (≤6 h, n = 6), B (6–24 h, n = 11), and C (>24 h, n = 11). Figure 1a and b illustrates the representative computed tomography or magnetic resonance images. The control group comprised two men and three women, with a mean age of 42.00 ± 17.9 years. Subgroup A comprised six men with TBI, with a mean age of 50.33 ± 14.1 years and a mean Glasgow Coma Scale (GCS) score of 8.0 ± 4.0; subgroup B comprised eight men and three women, with a mean age of 56.64 ± 11.70 years and a mean GCS score of 7.27 ± 3.22; and subgroup C comprised 10 men and one woman, with a mean age of 56.64 ± 8.86 years and a mean GCS score of 7.82 ± 3.06. No significant differences were observed among the groups in terms of age (P = 0.130), sex (P = 0.071), and GCS score (P = 0.890).

Temporal changes in brain pathological injury and cerebral edema after TBI

The pathological changes in the injured brain tissue were visualized using HE staining. Figure 1c illustrates that the morphology and structure of the brain tissues and neurons in the control group were normal and well-organized, accompanied by a dense interstitium without edema. The neurons in subgroup A were slightly swollen, with mild vacuolar degeneration. The neurons in subgroup B were considerably swollen compared with those in subgroup A. In subgroup C, the vacuolar degeneration of neurons was further worsened, resulting in considerable cell body swelling, an enlarged intercellular space, and a high number of degenerated and necrotic neurons. Nissl staining was also performed to identify neuronal injury. Figure 1d and e illustrates that the Nissl bodies in the control group were stained blue, with regular shapes and large quantities; this indicates a strong ability to synthesize proteins. However, compared with the control group, the size and quantity of Nissl bodies progressively decreased in subgroups A, B, and C. In particular, Nissl bodies almost disappeared in subgroup C, with a high degree of neuronal death (P < 0.05). Furthermore, compared with the control group, the brain water content significantly increased in all subgroups (P < 0.05), with brain water content exhibiting an increasing trend in subgroups A, B, and C (P < 0.05) (Fig. 1f). Collectively, these results suggest that brain damage and cerebral edema progressively worsen with TBI progression.

Autophagosome accumulation after TBI

Transmission electron microscopy, the ‘gold standard’ for identifying autophagic vesicles, was performed to visualize autophagosomes. Figure 2a demonstrates that the morphology of the nerve cells in the control group was as expected, with no double-membrane autophagic vacuoles. Surprisingly, double-membrane vacuoles containing residual digestive structures were observed in the injured brain tissue; this suggests an increase in autophagosomes after TBI. In addition, damaged nerve cells and a swollen cytoplasm were observed. To determine whether autophagosomes are increased in neurons after TBI, dual immunofluorescence staining was performed using antibodies against LC3 (a marker of autophagosome formation) and NeuN (a neuronal marker). Figure 2b demonstrates that LC3-positive neurons significantly increased in the TBI group compared with the control group. Furthermore, dual immunostaining was performed utilizing antibodies targeting LC3 and CTSD. We observed a reduced proportion (48%) of LC3-positive cells exhibiting co-localization with CTSD in injured brain tissue compared to the control group (97%) (Fig. 2c). This observation suggests a potential association between lysosomal dysfunction and the accumulation of autophagosomes following TBI.

Fig. 2.

Autophagosome accumulation in the human brain after TBI. (a) Electron micrographs showing increased autophagic vacuoles (highlighted by green squares) in the cytoplasm 6 h after TBI. (b) Dual immunostaining of NeuN (in red) and LC3 (in green), followed by the quantitative assessment of LC3-positive neurons in the human brain 6 h after TBI. Scale bar: 20 µm. (c) Double-immunostaining of CTSD (in red) and LC3 (in green), followed by quantitative assessment of LC3-positive neurons as well as cells exhibiting co-localization of LC3 and CTSD in the human brain 6 h after TBI. The percentage of overlap is indicated. Scale bar: 20 µm. *P < 0.05 compared with the control group; # P < 0.05 compared with the previous group.

Expression of marker proteins related to autophagic flux after TBI

To determine the state of autophagic flux after injury, western blotting and immunohistochemistry were performed to assess the changes in the levels of autophagy marker proteins over time in the injured brain tissue. Beclin-1 (indicative of autophagosome nucleation) and Atg5 (involved in autophagosome elongation) levels significantly increased in the injured subgroups compared with the control group (P < 0.05). However, their levels were not significantly different among the three TBI subgroups from 6 h after injury (P > 0.05), indicating increased autophagy induction in the early stage of TBI but without significant progression with TBI development (Figs. 3a–c and 4a–c). Furthermore, the levels of LC3-II, indicative of autophagosome numbers, and P62, a commonly used autophagic degradation marker to assess autophagic flux, were significantly higher in the TBI group than in the control group (P < 0.05); they exhibited a time-dependent increase after injury (P < 0.05) (Figs. 3a, d and e and 4a, d and e). Meanwhile, immunofluorescence assays revealed that LC3 and p62 colocalization was higher in neural cells from the injured brain tissue compared with those from the control; this increased over time after injury (Fig. 4f and g). However, the levels of CTSD, a lysosomal enzyme used to evaluate lysosomal function, were notably decreased after TBI, which further decreased with TBI progression (Fig. 3a and f). The expression patterns of LC3-II, P62, and CTSD suggest impaired lysosomal function and the inhibition of autophagic flux after TBI, resulting in evident autophagosome accumulation and progressively deteriorating characteristics with TBI progression.

Fig. 3.

Changes in autophagy-related proteins after TBI. (a) Representative Western blots of Beclin1, Atg5, LC3, P62, and CTSD. (b–f) Quantification of autophagy-related proteins. GAPDH is used as the loading control. *P < 0.05 compared with the control group; #P < 0.05 compared with the preceding group.

Fig. 4.

Immunohistochemical analysis and immunofluorescence staining of autophagy-related proteins. (a) Representative images from the immunohistochemical assays targeting Beclin-1, Atg5, LC3, and p62. Scale bar = 50 µm. (b–e) Quantification of Beclin-1, Atg5, LC3, and p62 protein levels (presented as IOD). (f) Representative images showing the immunofluorescence staining for LC3 and P62. Scale bar = 20 µm. (g) Quantification of cells positive for only LC3 and both LC3 and P62. *P < 0.05 compared with the control group; #P < 0.05 compared with the preceding group.

Correlation between autophagy-related proteins and neuronal injury and brain edema

Correlation analysis of the levels of autophagy-related proteins (detected via western blotting) with brain water content and Nissl bodies was conducted to elucidate the relationship between autophagy marker proteins and brain edema and neuronal injury. Figure 5 illustrates that Beclin1 levels were not correlated with Nissl bodies and brain water content (ρ = −0.2595, P = 0.1514; R = 0.1367, P = 0.4556). Similarly, Atg5 levels exhibited no correlation with Nissl bodies and brain water content (ρ = −0.2812, P = 0.1190; R = 0.3467, P = 0.0519). However, LC3-II and P62 levels were negatively correlated with Nissl bodies (ρ = −0.8212, P < 0.001; ρ = −0.7931, P < 0.001) but positively correlated with brain water content (R = 0.5454, P = 0.001; R = 0.6018, P = 0.0004). Conversely, CTSD levels were positively correlated with Nissl bodies (ρ = 0.8779, P < 0.0001) but negatively correlated with brain water content (R = 0.7885, P < 0.0001). Collectively, these findings suggest that impaired lysosomal function, autophagosome accumulation, and autophagic flux disruption contribute to TBI pathogenesis.

Fig. 5.

Correlation analysis of autophagy-related proteins with neuronal injury and brain edema. (a) Correlation analysis between the levels of autophagy-related proteins and Nissl body contents in patients with TBI. (b) Correlation analysis between the levels of autophagy-related proteins and brain water content in patients with TBI. R represents Pearson’s correlation coefficient, and ρ represents Spearman’s correlation coefficient.

Discussion

In this study, we elucidated the status of autophagic flux in the human brain tissue after TBI. We observed that the expression of autophagic flux-related proteins such as Beclin-1, Atg5, LC3-II, and P62 significantly increased during the early stages after TBI. Furthermore, LC3-II and P62 levels progressively increased with TBI progression, whereas Beclin-1 and Atg5 levels remained stable. Our study findings suggest that the disruption of autophagic flux is owing to autophagosome overload because of enhanced induction and impaired autophagosome degradation in the early stage after TBI. Moreover, this dysfunction is primarily associated with the progressive aggravation of autophagosome degradation with TBI progression. Furthermore, CTSD levels gradually decreased after TBI; this suggests that lysosomal abnormalities contribute to the disruption of autophagic flux. To the best of our knowledge, our study is the first to investigate autophagic flux in human TBI samples, providing novel insights into the autophagy process after TBI.

TBI mechanically damages the brain tissue and causes cerebrovascular rupture and occlusion, starting a damage cascade involving biochemical, cellular, and pathological events. Studies indicate that aberrant autophagy contributes to traumatic injuries in the central nervous system [12]. However, the precise mechanism underlying autophagy after TBI remains controversial, exhibiting both advantageous and detrimental roles [14,23]. While some studies have revealed increased autophagy in animal and human TBI samples, others have revealed decreased autophagy in experimental TBI scenarios [24–30]. Moreover, some studies have proposed the beneficial effects of treatments aimed at either improving or inhibiting autophagy after TBI [12]. Nevertheless, increasing evidence emphasizes the need to evaluate the entire autophagic process for accurate assessments, leading to the introduction of autophagic flux in various autophagy-related investigations [17,31]. Recent studies suggest that impaired autophagic flux contributes to neuronal cell death in TBI models, but improved autophagic flux helps recover neural damage after TBI [15,32–37]. To further explore the state of autophagy after TBI in humans, we investigated autophagic flux in the injured human brain tissues after TBI.

Autophagy is a dynamic degradation process involving cargo movement throughout the autophagy system. Autophagic flux refers to the complete sequence of this process [31]. Autophagic flux comprises four sequential stages: induction and phagophore formation, autophagosome formation, autophagosome–lysosome fusion, and cargo degradation and recycling [38]. In this study, four marker proteins, namely, Beclin-1, Atg5, LC3-II, and P62, which characteristically represent the different stages, were selected to determine the state of autophagic flux after TBI in humans.

LC3, a mammalian homolog of Atg8, is an essential ubiquitin-like protein in autophagy. The ATG4 protease immediately cleaves newly synthesized LC3 at its C-terminus, producing the cytoplasmic LC3-I form. Then, the C-terminal glycine of LC3-I couples with the lipid phosphatidylethanolamine through a ubiquitin-like coupling mechanism to produce LC3-II, which is tightly bound to the membrane of autophagosomes [39]. LC3-II, which is part of the autophagosome membrane, is considered a key molecule for controlling autophagic flux; in general, its expression is used to monitor the number of autophagosomes and the degree of autophagic flux [40]. In the present study, we observed a time-dependent increase in LC3-II protein levels after TBI. Simultaneously, the number of LC3-positive neurons increased in the injured brain tissue after TBI. Transmission electron microscopy intuitively observed the increased autophagosomes after TBI. The increased autophagosomes suggest the upregulation or accumulation of autophagosomes in the human brain tissue after TBI.

P62, a multifunctional adaptor protein, transports ubiquitinated substrates such as damaged organelles and toxic proteins to the autophagosomes. Subsequently, autophagosomes merge with the lysosomes to form autophagolysosomes, eventually clearing p62 and ubiquitinated substrates. Therefore, P62 accumulation indicates impaired autophagosome degradation [41]. In the present study, we observed that P62 protein levels and LC3 and p62 colocalization significantly increase throughout TBI, indicating a gradual decrease in autophagic degradation after TBI, thereby resulting in autophagosome accumulation. Previous studies have revealed that in mice with TBI, lysosomal dysfunction contributes to impaired autophagic degradation to some extent [15,28]. Consistent with these findings, our study findings revealed a progressive decrease in the levels of CTSD, a lysosomal enzyme that directly participates in autophagy and is responsible for autophagosome degradation after TBI. Furthermore, there was a noticeable decrease in the immunofluorescence co-localization ratio of CTSD with LC3 following TBI, indicating that lysosomal dysfunction contributes to impaired autophagic degradation after TBI in humans.

Considering that increased autophagy induction can result in autophagosome accumulation, we measured the protein levels of Beclin1 and Atg5. Beclin1, similar to yeast Atg6/Vps30, participates in the initiation phase of early autophagosome formation, whereas Atg5 plays an essential role in phagophore elongation. These proteins function as indicators of the initiation and elongation phases of autophagic flux [42,43]. Western blotting revealed that the protein levels of Beclin1 and Atg5 significantly increased during the super-early stage after TBI. Nevertheless, Beclin1 and Atg5 protein levels remained unchanged during the pathological progression of TBI. These findings suggest that TBI leads to dysfunctional autophagic flux, resulting in autophagosome accumulation owing to increased autophagy induction and impaired autophagosome degradation during the super-early stage. Furthermore, with TBI progression, autophagosome accumulation primarily occurs because of the progressive impairment of autophagosome degradation owing to lysosomal dysfunction.

To investigate the association between autophagy and TBI, human brain specimens were subjected to HE and Nissl staining, and brain water content was measured. We observed a progressive increase in brain edema and neuronal damage after TBI. Furthermore, correlation analysis revealed that Beclin1 and Atg5 were not correlated with brain water and Nissl body contents. However, LC3-II and P62 were positively correlated with brain water content and negatively correlated with Nissl body content. Conversely, CTSD levels showed a negative correlation with brain water content and a positive correlation with Nissl body content. These findings suggest that impaired autophagic flux worsens brain edema and neural injury, consistent with previous observations in in vivo models [15,16,18,32].

In conclusion, our study findings suggest that autophagic flux is impaired after TBI in humans. Autophagosome overload resulting from dysfunctional autophagic degradation could be the possible underlying mechanism, exacerbating brain edema and neuronal injury after TBI. In future studies, the interplay between autophagosome overload and neuronal damage should be elucidated and validated.

Acknowledgements

The study was conducted with the support of the Hebei Key Laboratory of Vascular Homeostasis in Shijiazhuang, China. The authors express their gratitude to the laboratory for providing the necessary facilities for conducting this research.

This research was supported by the S&T Program of Hebei (No. 22377717D), the Hebei Provincial Natural Science Foundation of China (Grant No.H2020206437), and the Key Medical Scientific Research Project of Hebei Province of China (Grant no. 20220112;20230032).

GS and JL designed the research study. JL performed the research. JL, BS, and SF analyzed the data. Initials GS and JL wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.

Data generated and analyzed during the current study can be obtained from the corresponding author upon reasonable request.

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Second Hospital of Hebei Medical University.

Conflicts of interest

There are no conflicts of interest.