Investigating Hippocampal Proteome Dynamics in Aging Rats with Minimal Hepatic Encephalopathy via High-Resolution Mass Spectrometry

doi:10.21203/rs.3.rs-5107499/v1

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Investigating Hippocampal Proteome Dynamics in Aging Rats with Minimal Hepatic Encephalopathy via High-Resolution Mass Spectrometry

https://doi.org/10.21203/rs.3.rs-5107499/v1

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The aging population faces a gradual decline in physical and mental capacities, with an increased risk of liver cirrhosis and chronic liver diseases leading to hepatic encephalopathy (HE). The intertwining of physiological manifestations of aging with the pathophysiology of HE significantly impairs cognitive ability, reduces quality of life, and increases mortality. Hence, effective therapeutic intervention is imperative. The present study investigated the impact of minimal HE (MHE) on cognitive impairment in an aging rat population by analyzing hippocampal proteome dynamics. For this purpose, an old MHE rat model was induced via thioacetamide. The label-free LC‒MS/MS method was employed to explore hippocampal proteomic changes and associated dysregulated biological pathways. A total of 1533 proteins were identified, and among these, 30 proteins were significantly differentially expressed (18 upregulated, and 12 downregulated). Three upregulated proteins, namely, fetuin-A, p23, and intersectin-1 were selected and validated for their increased expression via western blotting and immunofluorescence analysis, which confirmed the mass spectrometry results. These proteins have not been reported previously in MHE cases. We also identified the possible dysregulated biological pathways associated with the differentially expressed proteins via Metascape, a network analysis tool. We found that the differentially expressed proteins may be involved in the generation of precursor metabolites and energy, the neurotransmitter release cycle, positive regulation of dendritic spine development, chaperone-mediated protein folding and protein stabilization. This study highlights the potential mechanisms underlying neurological dysfunction in the aging population with MHE and identifies novel therapeutic targets for improved disease management.

Minimal hepatic encephalopathy

Aging

Hippocampus

Mass spectrometry

Fetuin-A

P23

Intersectin-1

Over the last two decades, the problem of the aging population has become a major concern for both developed and developing countries. The rate of population aging is accelerating faster than it was in the past, and people around the globe are living longer than ever before. According to projections, the proportion of people who are 60 years of age or older will increase by twofold, accounting for almost 22% of the population by 2050 (Kanasi et al. 2016). As a result, the incidence of age-related disorders is increasing. In addition to classical age-related neurodegenerative illnesses, studies have shown that an increasing prevalence of liver cirrhosis and chronic liver disease (CLD) leads to hepatic encephalopathy (HE) in the elderly population. It severely affects quality of life and increases mortality due to a lack of effective therapeutic intervention. This calls for immediate attention from researchers and clinicians.

HE is characterized by cognitive and motor impairments, which vary with HE type, ranging from subclinical alterations to coma and eventually death (Cauli et al. 2009). Approximately 80% of cirrhotic patients develop minimal HE (MHE), whereas 30–50% develop overt HE (Poordad 2007; Bajaj 2008; Prasad et al. 2024). MHE impairs various neurological functions, affecting the performance of psychometric tests that assess working memory, psychomotor speed, visuospatial ability, and other functional brain measures, without any evidence of apparent or classical clinical manifestations (Montgomery and Bajaj 2011). These functions are also compromised as a function of age. The aging brain is associated with changes in size, cognition and vasculature. It causes a decline in the ability to perform cognitive tasks such as decision-making, working memory, and executive functions, which is caused primarily by altered neuronal structure, dysfunction of neural networks, and loss of synapses that are accelerated during age-associated diseases (Murman 2015). MHE also leads to a decline in cognitive ability and, importantly, deficits in learning and memory. Thus, aging exacerbates MHE because the pathophysiological manifestations of MHE overlap with the physiological manifestations of aging. Despite the growing burden of liver disease leading to MHE in the elderly population (Bajaj et al. 2020), there is a paucity of research on the pathological processes underlying age-related MHE progression and potential therapeutic interventions. This requires a deeper understanding of both disorders to unravel common and specific features in the neural systems associated with them.

In recent years, the use of “high-resolution accurate mass spectrometry” (HRAMS) has been increasingly applied in proteomics research, particularly in the context of complex biological systems such as the brain, to gain a deeper understanding of the molecular mechanisms of the pathophysiology of neurological disorders and identify potential therapeutic targets (Zhou et al. 2013; Prasad et al. 2024). In the context of age-related MHE, the application of HRAMS to analyze hippocampal proteome dynamics could greatly increase our understanding of the molecular underpinnings of the disease and facilitate the discovery of novel therapeutic targets. By integrating large-scale proteomic data with robust bioinformatic analysis, we can explore the molecular landscape of age-related MHE and uncover previously unrecognized therapeutic opportunities

As stated above, MHE and aging encompass common factors that primarily affect cognitive functions and contribute to various neural disorders (Felipo et al. 2015). Several brain regions are involved in these high-order cognitive functions. Among these regions, the hippocampus is the most sensitive and malleable brain region affected at an early stage. Therefore, we propose that studying alterations in the hippocampal proteome can help us identify proteins associated with cognitive dysfunction. This knowledge can also help us in the diagnosis and development of pharmacotherapies with careful consideration of age-related parameters.

This study used an aged rat model of thioacetamide (TAA)-induced MHE. Our group has developed a well-established model that effectively mimics liver failure, as observed in humans (Singh and Trigun 2010). TAA is a well-known hepatotoxin that damages hepatocytes in a dose-dependent manner and results in various grades of HE. In the present study, we confirmed the impact of MHE on aging rats via novel object recognition (NOR) rest, neuronal arborization in hippocampal neurons via Golgi Cox staining, and cell density in different hippocampal regions via Nissl staining. Next, we explored the hippocampal proteome dynamics involved in the pathophysiology of MHE in old rats compared with that in old control rats. For this purpose, we utilized a label-free LC‒MS/MS technique. To confirm the mass spectrometry (MS) findings, we utilized western blot and immunofluorescence methods. We also studied the cell-specific distribution of validated proteins by colocalizing them with neuronal and astrocytic markers. We also performed bioinformatic analysis of the significantly differentially expressed proteins via Metascape, which helped us identify the possible biological pathways related to the pathophysiological conditions of old rats with MHE (Fig. 1). This comprehensive examination provides novel perspectives on the fundamental causes of illness in old MHE rats and provides a way to identify previously unidentified therapeutic targets. These findings may aid in the development of therapeutic drugs designed to mitigate the effects of MHE in the aging population.

2.1 Chemicals

All the chemicals used in this study were of molecular biology grade. Most of the reagents, including thiourea, urea, CHAPS, Tris, dithiothreitol (DTT), ammonium bicarbonate (ABC), trichloroacetic acid (TCA), acetone (HPLC grade), formic acid (FA) and acetonitrile (ACN) (LC‒MS grade), were purchased from Sisco Research Laboratory (SRL), Mumbai. Iodoacetamide (IAA) was purchased from Sigma Aldrich, United States. The protease inhibitor cocktail was supplied by Puregene, Genetix Biotech Asia Pvt. Ltd., New Delhi. A BCA kit, MS-grade trypsin, and desalting column (C18 spin column) were purchased from Thermo Scientific in the United States.

2.2 Experimental animals and housing

The experiments were performed on old male Charles foster strain rats (16–18 months) weighing 250 ± 30 g. These rats were raised for breeding in the animal facility of the “Department of Zoology, Banaras Hindu University, Varanasi, India”. A controlled environment with air conditioning, a 12-hour light‒dark cycle, and ad libitum access to food and water was provided to the rats. Each cage housed three rats. The “Committee for Purpose of Control and Supervision of Experiments on Animals” (CPCSEA) guidelines were followed for animal maintenance. Furthermore, the Institutional Animal Ethical Committee (IAEC) of the Department of Zoology, Institute of Science, Banaras Hindu University, approved all the experimental techniques.

2.3 Induction of MHE in old rats and hippocampal tissue collection

The experiment involved two groups of old rats (16–18 months): an old control group and an old MHE group, with 26 old rats per group (total 52 rats). MHE rats received an intraperitoneal injection of TAA solubilized in normal saline (NS) at a dose of 50 mg/kg body weight once daily for 14 days, whereas control rats received normal saline (Mondal and Trigun 2015). Old control and old MHE rats were administered 5% dextrose solution containing 0.45% NaCl and 20 meq l-1 KCl in their drinking water to reduce body weight loss, hypoglycemia, and kidney dysfunction (Singh and Trigun 2010). Six rats (3 per group) were used for cellular morphological study, and six rats (3 per group) several rats were used for the neuronal arborization study. We used 10 rats (5 per group) to perform the neurobehavioral test. After 24 hrs of the last dose, 18 rats (9 rats per group) were sacrificed under anesthesia, their brains were dissected, and the hippocampus was removed and snap-frozen in liquid nitrogen. Twelve rat hippocampi (6 per group) were subjected to MS, and the remaining 6 (3 per group) were subjected to western blotting. For immunofluorescence, 12 rats (6 per group) were used.

2.4 Novel Object Recognition (NOR) Test for Assay Working Memory

To confirm the deficit in working memory, the NOR test was performed. This test utilizes the natural preference for novelty of rodents. The test was conducted for 3 days, starting with the habituation day, followed by the training day, and, finally, the test day. The NOR test uses an open square wooden box with dimensions of 46x46x46 cm, where the floor and walls are painted black. During the habituation phase, the rats were allowed to move around the empty NOR arena for 5 min for 2 consecutive days. The next day, during the training phase, the rats were trained with two objects placed diagonally in opposite quadrants of the NOR arena. The rats were allowed to explore both objects for 5 min. After 1 hr of the training phase, the rats were subjected to the test phase, where one of the objects was replaced by a new one, and the rats were again allowed to move around the objects. The total time spent with novel and familiar objects was recorded via a video camera mounted at a height of 150 cm from the wooden box. ANY-maze software was used to measure the exploration time of the rats toward novel and familiar objects. Working memory is assayed via the form discrimination index (DI), which is calculated as [(time spent toward the novel object - time spent toward the familiar object)/(total time spent toward both objects during the test phase)].

2.5 Nissl staining of the hippocampus

After being anesthetized, the rats were perfused with 1× PBS followed by 4% paraformaldehyde (pH 7.4). Coronal 25 µm-thick brain sections were cut with a cryostat. Slides containing sections were incubated in 1:1 alcohol/chloroform solution overnight for defating to prevent background staining. Then, the sections were rehydrated through an ethanol gradient in distilled water and submerged in 1% cresyl violet for approximately 15–20 min until the required staining depth was achieved. Following a rinse with distilled water and a dehydration process using graded alcohol, the sections were cleared in xylene. The sections were mounted in DPX and then cover slipped. Nissl-positive cells in the pyramidal layer of the medial CA1, CA3 and DG were observed for neuronal loss. Image analysis was performed via ImageJ software to determine the neuronal density of selected regions of the hippocampus.

2.6 Neuronal arborization of pyramidal neurons in the hippocampal region via Golgi Cox staining

The Golgi staining procedure was carried out via Zaqout and Kaindl's (2016) methodology with some modifications (Zaqout and Kaindl 2016). The dissected brain was immediately placed in impregnation solution, which was prepared by combining solutions of 5% (w/v) K₂Cr₂O₇, K₂CrO_4, HgCl2 and ddH₂O at a ratio of 5:5:4:10. The impregnation solution was removed after 24 hours, and fresh impregnation solution was poured into the same vial containing the brain tissue and kept at room temperature in the dark for 7 to 10 days. The tissue was kept in a tissue-protectant solution composed of phosphate buffer (0.1 M, pH 7.2), 0.1% PVP40, 30% sucrose and 30% v/v ethylene glycol. Later, floating slices of the brain with thicknesses of approximately 150 µm were cut via a vibrotome (Lieca). After that, the slices were developed, dehydrated in 50% ethanol, placed in a solution of chloroform and water (3:1), washed with water and then developed in the dark with 5% sodium thiosulfate, dehydrated with graded ethanol, cleared in xylene and mounted with DPX. Each slide was coded and subjected to blind analysis. Following scanning of the sections at lower magnification, neurons from the hippocampal CA1 region were selected randomly.

The neurons that fulfilled the following parameters were used for reconstruction: 1) consistent staining in all dendrites; 2) fully impregnated neurons; 3) the presence of neurons in relative isolation from other neighboring; and 4) neuron dendrites without truncated branches (Titus et al. 2007). The selected neurons were reconstructed at 400× magnification via a fully XYZ motorized microscope (BX51) (Olympus, Tokyo, Japan). After 3D tracing, Sholl’s analysis (Sholl, 1953, 1956) was performed via Neuroexplorer software, which was provided by MBF Bioscience, Williston, Vermont, USA. The 50 µm-diameter concentric circles that radiate from the soma center were placed on the tracing. The software evaluated the length of dendrites and the number of nodes in each subsequent segment. The analysis of the dendritic branching of apical and basal branches was conducted up to distances of 250 µm and 150 µm from the soma center, respectively. Ten neurons were traced from each animal that fulfilled the abovementioned conditions, with 3 rats/group, and the data were averaged. A total of thirty neurons from each experimental group were investigated.

2.7 Sample preparation for the LC‒MS/MS analysis

2.7.1 Protein extraction

Old control and old MHE rat hippocampi were used to generate 10% protein lysates. Urea lysis buffer, which included 2 M thiourea, 7 M urea, 4% CHAPS, 10 mM Tris, 0.1 mM PMSF, and 65 mM DTT, was used to prepare the lysates via a handheld micro homogenizer. The samples were sonicated on ice and then centrifuged at 10,000 × g at 4°C for 15 minutes. Protein purification was performed via the TCA‒acetone precipitation method. The lysate was mixed with absolute TCA and ice-cold absolute acetone at a ratio of 1:8:1 for precipitation. After an hour of incubation at -20°C, the mixture was centrifuged at 18,000 × g for 15 minutes at 4°C. Chilled with 100% acetone was used four times to wash the resulting pellets. The pellets were then air-dried and kept for future use at -80°C.

2.7.2 Protein digestion

The protein pellets were solubilized in a urea solution that also contained 100 mM ABC, 5 mM DTT, and 8 M urea. A BCA kit was used for protein estimation. Coomassie Brilliant Blue (CBB) staining was used after performing 12% SDS‒PAGE to evaluate the quality of the proteins (Supplementary Fig. S1). Fifty micrograms of protein samples that had been purified were reduced and alkylated. An equivalent quantity of DTT was added to quench the alkylation reaction. With the use of a dilution buffer that contained 2 mM calcium chloride and 50 mM ABC buffer, the urea concentration in the solutions was diluted to 1 M. The reaction mixture was then treated with MS-grade trypsin at an enzyme-to-protein ratio of 1:40. The combination was then incubated for 16 hours at 37°C to facilitate the digestion of proteins. The reaction mixtures were lyophilized via a speed vac, and trypsin digestion was stopped. After being reconstituted in 0.1% FA, lyophilized peptides were purified via a C-18 column. The peptides were again lyophilized via a vacuum concentrator and stored at -80°C until further examination.

2.8 Label-free LC‒MS/MS analysis

After being desalted and lyophilized, the peptides were reconstituted in 0.1% (v/v) formic acid (FA). The concentration of peptide was estimated via the use of a NanoDrop instrument via the scopes approach and the A205/280 program. One µg of peptide was injected into the LC column, and the easy nano-LC 1200 system was used to separate the mixture across an LC gradient (5%, 30%, 60% and 90% acetonitrile (ACN) in 0.1% FA) for 2 hrs at a flow rate of 0.3 µl/min. The MS analysis was carried out via a ThermoFisher Scientific Orbitrap Fusion “Tribrid Mass Spectrometer”. Bovine serum albumin was used to evaluate the instrument quality by running it at the beginning and end of each set of MS runs. With a mass resolution of 60,000 at the MS level and a mass scan range of 375–1700 m/z, the data were acquired in data-dependent acquisition mode. A mass tolerance of 10 ppm was used, and the dynamic exclusion window was set to 40 seconds. During the Data Dependent MSⁿ Scan, high collision-induced dissociation was employed with a fixed collision energy mode of 30%. The parameters for the detector were “orbitrap in MS, 60,000, and 15,000 MS/MS”. Additionally, a maximum injection time of 30 ms was selected.

2.9 Proteomic data processing and statistical analysis

Using the UniProt database (Coudert et al., 2023) of Rattus norvegicus as a reference, we employed MaxQuant 2.1.1.0 (Tyanova et al., 2016) to analyze MS data to identify proteins. For trypsin, a maximum of two missed cleavages were selected. The mass tolerance parameter for MS was 10 ppm, and that for MS/MS was 0.1 Da. To guarantee a high-confidence data pool, we implemented a 1% false discovery rate for the protein and peptide-spectrum match (PSM) levels. All acquired MS proteomics data have been uploaded to the server of the ProteomeXchange Consortium with the dataset identifier PXD054493.

We used MetaboAnalyst 5.0 (Pang et al., 2022) for the statistical analysis and normalization of the detected proteins to preserve data integrity. The comma-separated values (.csv) file with samples in the columns was subjected to MetaboAnalyst, and proteins with more than 30% missing values were eliminated. We utilized the K-nearest neighbor single imputation method (KNN-SMP) algorithm to impute the missing values of the proteins. This method was selected because of its effectiveness in managing occasionally missing data while maintaining the original data distribution. During this phase, we refrained from using non-QC-based data filtering. The data were normalized by the sample median and transformed to log10. After that, a sample-wise validation of the data quality was conducted via a Pearson r correlation analysis (Supplementary Fig. S2). We used a t test to identify the significantly altered proteins in old MHE rats, where a p value ≤ 0.05 was used. The visualization of differential expression was made easier with a volcano plot, where a threshold of a fold change of 1.25 and a p value ≤ 0.05 was used. Normalized MS data were subjected to partial least squares discriminant analysis (PLS-DA) to further explore the perturbations observed in the sample groups.

2.10 Biological Pathway Enrichment and Interactome Analysis

Using the Metascape platform (version 3.5.20240101) (Zhou et al., 2019), we conducted a thorough gene ontology (GO) enrichment analysis of 30 significantly dysregulated proteins to understand their possible biological functions. We carefully examined changes in protein expression in old MHE rats compared with that in old control rats and identified a subset of proteins whose expression was significantly disrupted, either by upregulation or downregulation.

2.11 Validation of the identified hippocampal proteins

To validate the MS results, we selected three proteins from among the upregulated proteins and confirmed their expression via western blot and immunofluorescence methods.

2.11.1 Western blot analysis

The tissue was homogenized in NP40 buffer with protease inhibitor cocktail to extract the old control and the MHE hippocampus protein (n = 3 for each group). A BCA kit was used to estimate the protein concentration. The same quantity of protein from the old control and old MHE groups was separated via 10% SDS‒PAGE, transferred to a polyvinylidene difluoride (PVDF) membrane for an overnight period at 4°C and 25 mA current, and then blocked for one hour with 5% nonfat skim milk in 1× TBST. Then, the samples were incubated overnight at 4°C with the following specific primary antibodies: fetuin A (1:1000 cat# PA5-78744, Invitrogen), P23 (1:1000 cat# PA5-29547, Invitrogen), and intersectin-1 (1:1000 cat# PA5-115432, Invitrogen). The next day, the unbound antibodies were washed away with TBST, incubated for one hour with secondary antibodies (HRP-conjugated) (1:2000, Cat#: 7074P2, Invitrogen), and then washed again with TBST. The signal was then detected with an ImageQuantTM (Ai680) using enhanced chemiluminescent (ECL) reagent (170–5060, Bio-Rad). Blots containing each protein were analyzed via FIJI ImageJ. The expression of each protein was normalized by dividing its densitometric value by the densitometric value of GAPDH.

2.11.2 Immunofluorescence analysis

After 24 h of treatment, 12 rats (6 per group) were anesthetized and perfused with a 4% paraformaldehyde solution. Twenty-five-micron-thick sections were cut via a cryostat (HM525 NX, Thermo Scientific), and the following primary antibodies were used for immunofluorescence detection: fetuin A (1:200 cat# PA5-78744, Invitrogen), P23 (1:400 cat# PA5-29547, Invitrogen) and intersectin-1 (1:400 cat# PA5-115432, Invitrogen). The final immunofluorescence signal was detected via an Alexa Fluor 488-conjugated secondary Ab (1:2000, Cat#: 4412; CST). The immunofluorescence signal was observed and captured via a microscope (BX63 Microscope, Olympus). Using FIJI ImageJ software, fluorescence images were examined, and the fluorescence intensities of the old MHE and the old control were evaluated.

2.11.3 Colocalization of Fetuin A, P23, and Intersectin-1 with NeuN and GFAP

A colocalization study was conducted to detect the cell-specific distribution of fetuin A, P23, intersectin-1 in microglia and neurons. To achieve this goal, we incubated primary antibodies against the candidate proteins; fetuin A (1:200 cat# PA5-78744, Invitrogen), P23 (1:400 cat# PA5-29547, Invitrogen) and intersectin-1 (1:400 cat# PA5-115432, Invitrogen) along with primary antibodies against GFAP (1:1000, cat# 3670S, CST) and NeuN (1:1000, cat# 94403S, CST). The final immunofluorescence signal was detected via an Alexa Fluor 488-conjugated secondary Ab (1:2000, Cat#: 4412, CST) along with an Alexa Fluor 555-conjugated secondary Ab (1:2000, Cat# 4409S, CST).

2.12 Statistical analysis

MetaboAnalyst 5.0 was utilized to analyze the proteomic data (details are given in Sect. "Proteomic data processing and statistical analysis"). Western blot analysis, histological examinations, and the NOR test were conducted via an unpaired Student's t test to identify significant differences between the old control and old MHE groups. A p value of less than 0.05 was considered statistically significant.

3.1 Impact of MHE on memory function in aging rats

We performed the NOR test to assess impaired cognitive function in old MHE rats. This test depends on the rodent's innate response to spend more time exploring the novel object than the familiar one. We observed that old MHE rats spent comparable amounts of time exploring both objects, whereas old control rats preferred to explore the new object over the familiar object and took significantly more time to explore the novel object, as shown in Fig. 2a & b. As a result, the discrimination index of old MHE rats was significantly (p < 0.01) lower than that of old control rats, suggesting that MHE caused a decrease in the working memory performance of aging rats.

3.2 Neuro-morphological changes in the hippocampus

Nissl staining was performed to observe the neuronal cell population by specifically staining the Nissl bodies. Photomicrographs of the Nissl-stained sections revealed a notable decrease in cell density in all three parts of the hippocampus (CA1, CA3 and DG) in the old MHE rats compared with the old control rats, suggesting increased neuronal cell death (Fig. 3a). The CA3, CA1, and DG regions also presented a thinner molecular layer. Using ImageJ software, a quantitative study of Nissl-stained cells revealed a significant (p < 0.05) decrease in the number of neurons in all three hippocampal regions of old MHE rats (Fig. 3b-d).

3.3 Dendritic arborization of the hippocampal neurons

In our investigation, we performed three-dimensional digital reconstructions of Golgi-cox-stained hippocampal pyramidal neurons to investigate their degree of dendritic arborization. These reconstructions are depicted in Fig. 4a. Compared with those in the old control group, the neurons in the old MHE group were shorter and had fewer branches, which is indicative of neuronal atrophy. Sholl analysis revealed that in old MHE rats, the length of the apical dendrites decreased with increasing radial distance from the soma (Supplementary Fig. S3a). Similarly, the basal dendrite length decreased (Supplementary Fig. S3b). Remarkably, a significant reduction was observed in the span of 150–300 µm. Additionally, there was a significant reduction in the sum length of the apical and basal dendrites in the old MHE rats compared with those in the old control rats (Fig. 4b-c). There was also a reduction in the total branching points of the apical and basal dendrites of the old MHE rats compared with those of the old control rats (p < 0.001) (Fig. 4d-e). In conclusion, we observed that in the hippocampus of old MHE rats, there was an overall reduction in the degree of dendritic arborization of pyramidal neurons compared with that in the hippocampus of old control rats.

3.4 Discovery-based proteomics-LC‒MS/MS

The hippocampal proteome was the subject of this attention because of its relevance in biological studies, especially because of its connection to learning and memory processes. A total of 1533 proteins were initially identified. The total number of proteins was reduced to 1325 proteins after 208 proteins that were duplicates or had a constant value throughout the samples were removed. After a missing value imputation at the 30% threshold, 1002 proteins were retained for further analysis.

To visualize the top 50 highly dysregulated proteins, a heatmap visualization was constructed. In this diagram, experimental groups are represented by columns, whereas individual proteins are represented by rows. Red denotes upregulation, and blue denotes downregulation (Fig. 5a).

We found 30 proteins whose expression was significantly dysregulated in old MHE rats compared with that in old control rats in our volcano plot study. These 30 proteins can be further studied to explore their biological functions and potential role in disease progression. The selected 30 proteins satisfied the stringent p value criterion of 0.05 and a log1.25-fold change, as specified in Fig. 5b. When comparing fold changes to statistical significance and visualizing differential protein expression, volcano plots are quite helpful. The identified proteins were divided into two primary categories: downregulated (12) and upregulated (18). Among the 18 upregulated proteins were lysosomal alpha-glucosidase, Caskin 1, fetuin A, DnaJ homolog subfamily C member 5, prostaglandin E synthase 3 (cPGES) and intersectin-1. In contrast, the 12 downregulated proteins included apolipoprotein E (Apo-E), ADP-ribosylation factor 1, paraspeckle component 1 and striatin.

PLSDA was applied to the selected 1002 proteins. PLSDA helps to visualize the overall variance in the dataset, and its score plot helps us identify the characteristics that differentiate old control rats from old MHE rats. Compared with those of the old control rats, the features of the old MHE rats create a different group, as shown in Fig. 5c.

3.5 Functional enrichment analysis and protein‒protein interaction

We used Metascape to perform a thorough enrichment analysis to obtain insights into the functional implications of the dysregulated proteins. Ontology sources such as the KEGG pathway, CORUM, Reactome gene sets and GO biological processes were used to perform biological pathway enrichment analysis via Metascape. We examined the functional enrichment and protein‒protein interactions among 30 proteins shown to be differentially expressed in the hippocampus of MHE-induced aging model rats to investigate the functional consequences of MHE in aging rats.

Our analysis revealed several GO biological process-based clusters that may be dysregulated in old MHE rats. Notably, these genes included precursor metabolites and energy (8 proteins), positive regulation of dendritic spine development (4 proteins), positive regulation of endocytosis (5 proteins), glycogen metabolic process (3 proteins), protein stabilization (3 proteins), locomotory behavior (3 proteins) and lipid biosynthetic process (4 proteins). Analysis of the enriched Reactome gene sets also revealed significant dysregulation in various processes, including the neurotransmitter release cycle (3 proteins), neutrophil degranulation (6 proteins) and metabolism of RNA (6 proteins) (Fig. 6a).

A network plot was created using a subset of enriched terms to provide additional insight into the relationships among the terms. For this analysis, the top p value phrase was chosen from each of the 20 clusters. Every node in the network, colored by its corresponding cluster ID, represents an enriched phrase. This methodology facilitated an all-encompassing and meticulous visualization of the complex interrelationships and associations among several dysregulated proteins and their corresponding processes, establishing the foundation for subsequent therapeutic investigations (Fig. 6b).

3.6 Validation of the candidate protein via western blotting and spatial distribution via immunofluorescence

Proteomic analysis revealed 30 proteins whose expression was significantly altered at the > 1.25-fold change threshold in the hippocampal tissue of old MHE rats. We performed western blot analyses of fetuin A, P23 and intersectin-1 to validate the MS results. We detected a significant increase in the expression levels of these proteins in old MHE rats compared with those in old control rats, which was in agreement with the MS findings shown in Fig. 7. To investigate the spatial distribution of these proteins in the hippocampus, immunofluorescence was used. We found that these three proteins were more highly expressed in the CA1, CA3 and DG regions of the hippocampus of old MHE rats than in those of old control rats (Fig. 8).

3.7 Cell-specific distribution of validated proteins

We examined the colocalization of fetuin-A, P23, and intersectin-1 with NeuN and GFAP in the CA3 and DG regions in old MHE rats and old control rats to determine their cell-specific expression, and we found that, with the exception of fetuin-A, which colocalized with only NeuN, the other two proteins colocalized with both the cell-specific markers NeuN and GFAP (Fig. 9).

Aging is associated with a high risk of disease development. Among these diseases, those predominantly observed in the aging population include CLD and cirrhosis. A major complication of this disease is the development of a neurological disorder, HE, primarily MHE, which affects cognitive function and is essential for functional independence and effective communication with others. Understanding the cognitive impairment that is common in older individuals with cirrhosis is challenging because of their advanced age (Kanwal et al. 2009). In this context, studying the relationship between hippocampal structure and function in the presence and absence of liver disease in the aging brain can help to provide insight into disease pathogenesis. The hippocampus is subjected to early changes during insult; hence, it is an ideal brain region to study.

Aging affects hippocampal brain regions. It leads to changes in cellular density and dendritic arborization, which is implicated in decreased learning and memory function (age-dependent changes). However, during liver disease-induced MHE in older individuals, the intensity of such changes increases, as we have shown in our study. Our analysis of Nissl-stained sections revealed a decrease in the number of neuronal cells in the CA1, CA3 and DG regions of the hippocampus in old MHE rats compared with those in old control rats (Fig. 3). These findings shed light on the structural changes occurring in neurons and synapses that drive the observed cognitive and memory problems associated with old MHE rats.

Examination of dendritic arborization via Golgi-Cox staining of CA1 pyramidal neurons from old MHE rats revealed significant reductions in dendritic length and number of nodes compared with those in the control group (Fig. 4). This may be a factor for the decline in hippocampal-associated behavior, particularly learning and memory.

Taken together, these observations revealed decreased neuronal arborization and neuronal cell numbers in the hippocampi of old MHE rats compared with those in the hippocampi of old control rats, confirming how MHE further impacts the aging brain and is deleterious. These results also provide evidence that the hippocampus is a potential target for global proteome studies and the identification of new therapeutic targets.

The above findings were corroborated with behavioral tests. We performed the NOR test, which is frequently recommended for assessing learning and memory deficits in rodent models. When hippocampal memory function was assessed via the NOR test (Antunes & Biala, 2012), significant differences were found between the two experimental groups. Old control rats explored new objects for a longer period of time than they did when the objects were familiar. On the other hand, old MHE rats showed almost comparable exploration periods for both unfamiliar and familiar objects (Fig. 2b), leading to a decreased discrimination index (Fig. 2c). The impaired ability of old MHE rats to discriminate between new and familiar objects suggests impaired memory function, making it more difficult for them to retrieve previously acquired information. The behavioral observations supported the findings of the neuroanatomical and histological studies.

Our present study expanded beyond targeted investigations by utilizing LC‒MS/MS for label-free measurement. This methodology made it easier to obtain large datasets. Subsequent bioinformatic studies of these datasets helped identify the altered proteins that were crucial for determining the different biological pathways, processes, and functional networks involved in aging rats with MHE, which were previously unconnected to decreased cognitive function.

Our proteomics analysis via MetaboAnalyst identified 30 dysregulated proteins. Functional enrichment analysis via Metascape revealed several deregulated pathways (Fig. 6), including precursor of metabolites and energy, positive regulation of dendritic spine development, positive regulation of endocytosis, glycogen metabolic process, protein stabilization, locomotory behavior and lipid biosynthetic process, neurotransmitter release cycle, neutrophil degranulation, metabolism of RNA and legionellosis. Several proteins related to precursors of metabolites and energy, including lysosomal alpha-glucosidase, ATP synthase subunit O, P23, isocitrate dehydrogenase [NAD] subunit beta and acetyl-CoA acetyltransferase, have been identified. Those involved in the positive regulation of dendritic spine development include CSP, P23, Intersectin-1, Rab GDP dissociation inhibitor beta and ADP-ribosylation factor 1. Proteins such as fetuin A, CSP, and ARF1 were shown to be associated with the positive regulation of endocytosis and neutrophil degranulation.

Among the downregulated proteins, striatin, which is named after the striatum, is abundantly expressed in the brain. It is a cell‒cell junctional protein that maintains correct cell adhesion and may play a role in establishing connections between tight junctions and adherens junctions (Lahav-Ariel et al. 2019). Alterations in the assembly of junction-associated proteins and their function in the brain significantly compromise the permeability of the blood–brain barrier (Stamatovic et al. 2016). Another group of scientists reported that the downregulation of striatin in motor neurons leads to impaired dendrite growth, suggesting its role in the growth of dendrites and remodeling (Hwang and Pallas 2014). It has been reported that there is compromised blood‒brain permeability in a model of HE (Ferenci 2017), and downregulation of striatin might indicate its ability to compromise blood‒brain permeability and promote dendrite remodeling.

Another downregulated protein is ADP ribosylation factor (ARF 1), which is a guanine nucleotide-binding protein that plays a crucial role in vesicular trafficking (Pacheco-Rodriguez et al. 1999). ARF1 is associated with the Golgi apparatus in neurons and is responsible for organizing coat proteins to be sorted and transported in the cell. Ananya Bansal, along with her group in 2019, reported that decreasing APP expression decreases the secretion of amyloid peptides. These effects appear to be due to the downregulation of ARF1, which is crucial for the secretory pathway (Bansal et al. 2019). Similarly, ARF1 has been reported to regulate dendritic spine size and the trafficking of AMPAR receptors and is involved in NMDAR-mediated signaling (important for synaptic plasticity and LTD). The downregulation of ARF1 in our study might play a crucial role in disease progression via the abovementioned signaling pathway.

From the upregulated proteins, we selected three candidate proteins for validation on the basis of their potential role in the pathophysiology of aging with MHE, which was based on their involvement in other neurological disorders that have been well studied. We validated 3 candidate proteins, namely, fetuin-A, P23, and intersectin-1, via western blotting and double immunofluorescence for spatial distribution in the hippocampus. However, studies of the pathogenesis of these proteins in old MHE rats are limited. Fetuin-A, which is necessary for several physiological functions, is produced by the liver. A verified diagnosis of nonalcoholic fatty liver disease (NAFLD) is linked to a modest increase in fetuin-A serum levels, according to scientific research. Fetuin-A regulation is linked to a number of pathophysiological factors that result in liver issues, such as insulin receptor signaling deficits, adipocyte malfunction, hepatic inflammation, fibrosis, triacylglycerol synthesis, macrophage invasion, and TLR4 activation (Sardana et al. 2021). The biochemical pathways that are activated by cytokines, tumor necrosis factor α (TNFα), interleukins (ILs), fatty acid binding protein 4, monocyte chemotactic protein-1, chemokines, adipokines, etc., are all directly affected by fetuin-A. Recent research has demonstrated that high levels of fetuin-A stimulate the expression of inflammatory cytokines in mouse macrophages and adipocytes (Lee et al. 2019).

The hepatokine fetuin-A has been shown to increase insulin resistance and adipose tissue inflammation through toll-like receptor 4 (TLR4) when it is combined with free fatty acids (FFAs) (Lee et al. 2017). FFAs and chronic low-grade inflammation were previously thought to be unrelated, but new research has demonstrated that the liver secretory protein fetuin-A functions as an adapter protein between FFAs and TLR4 (Heinrichsdorff and Olefsky 2012). Another group of researchers reported that fetuin-A has anti-inflammatory benefits in the treatment of traumatic brain injury (TBI) by promoting the Nrf-2/HO-1 pathway, which in turn inhibits microglial activation, decreases the production of necrosome complexes, and suppresses oxidative stress. They also discovered that fetuin-A supplementation decreases the release of cytotoxic chemicals from injured microglia, hence reducing neuronal death. They concluded that fetuin-A may be a useful therapeutic agent for treating TBI (Zhao et al. 2022). In our study, fetuin-A was found to be significantly upregulated in old MHE rats (Fig. 7a & 7d). In addition, it was also overexpressed in the CA1, CA3, and DG regions of the hippocampus of old MHE model rats (Fig. 8a-d) and colocalized with NeuN (Fig. 9a), which is a neuron-specific “marker” that occurs exclusively in postmitotic neurons (Duan et al. 2016). In addition to the neuronal cells, we attempted to colocalize fetuin-A with GFAP (an astrocyte-specific marker) and found no colocalization of fetuin-A with GFAP-positive cells, such as astrocytes. These results indicate that fetuin-A may perform different functions in old MHE rats than those reported in various studies, which revealed its colocalization with GFAP (Heinen et al. 2018).

P23, or cytosolic prostaglandin E2 synthase 3 (cPGES), is involved in the cascade of arachidonic acid and in the constitutive expression of prostaglandins such as PGE₂ (Xu et al. 2021). Cyclooxygenase I (COX I) is preferentially involved in the function of cPGES, which leads to the production of prostaglandin PGE₂ and COX I-derived products involved in the initial phase of acute-phase inflammation, whereas COX II upregulation starts after some time (Gomez et al. 2013). In our study, cPGES was upregulated in old MHE rats compared with old control rats (Fig. 7b & 7e). Additionally, it was also overexpressed in the CA1, CA3, and DG regions of old MHE rats (Fig. 8e-h), and it was also localized to NeuN and GFAP (Fig. 9b), confirming its expression in neurons as well as astrocytes.

Intersectin-1 is a multidomain scaffolding protein involved in synaptic vesicle recycling. It is a part of the Reelin pathway, which controls synaptic plasticity and neuronal migration in the hippocampus (Jakob et al. 2017). A group of researchers have shown that intersectin-1 promotes the endocytic process by directly interacting with the clathrin adaptor complex AP2, which regulates the initial steps of clathrin-mediated synaptic vesicle recycling (Pechstein et al. 2010). Another group has shown that intersectin-1 is involved in the regulation of synaptic vesicle replenishment through interactions with synapsin I. This functional association between synapsin 1 and intersectin-1 may underlie its association with learning and memory and the progression of some neurological disorders (Gerth et al. 2017). Neurotransmission depends on exocytosis of the synaptic vesicle at the active zone, and subsequent replenishment via endocytosis requires a tight connection between synaptic vesicle release and its recycling, which is provided by the scaffolding protein, and intersectin-1 is a suitable protein for fulfilling this role (Gubar et al. 2013). Increased expression of intersectin-1 may indicate fast endocytosis of the synaptic vesicle at the presynaptic zone, which might be related to the development of neurological problems or excitotoxicity-induced neurological conditions. In our study, intersectin-1 was upregulated (Fig. 7c & 7f) and was also overexpressed in the CA1, CA3, and DG of old MHE rats compared with those of old control rats (Fig. 8i-l). Additionally, intersectin-1 was detected via double immunofluorescence and was found to be co-expressed with both NeuN and GFAP (Fig. 9c), which indicates that this protein was expressed in both neurons and astrocytes.

The study provides valuable insights but has several limitations. First, the modest sample size (n = 6) means that we must interpret the findings cautiously. While aging rats are used as models because of their physiological similarities to humans, we should approach direct extrapolation to human biology carefully because of interspecies differences. Despite its high sensitivity, the label-free LC‒MS/MS technique may capture only a portion of the proteomic landscape, especially ultralow-abundance proteins. Additionally, the integration of machine learning introduces the risk of overfitting, highlighting the need for rigorous validation. Bioinformatics tools such as Metascape and STRING, while useful, are constrained by existing databases and may overlook certain protein interactions. The focus on the hippocampus may also limit the understanding of contributions from other brain regions. Moreover, external variables such as environmental conditions or diet could introduce confounding factors. Finally, further validation of the identified proteins and pathways, especially in human models, is crucial for obtaining a complete understanding of disease biology.

In light of the extensive proteomic analysis presented, our study highlights the profound proteomic alterations in aging rats with MHE. The differential expression of proteins, ranging from proteins that may be responsible for compromised blood brain permeability, such as striatin, to proteins that may induce inflammatory mechanisms, such as Fetuin A and P23, delineates the intricate cellular modifications and challenges presented by MHE in the aging population. These findings not only offer a deeper understanding of MHE pathology in the aging population but also set the stage for potential therapeutic interventions. The identified proteins and associated pathways serve as promising avenues for further research, fostering the prospect of innovative treatments and interventions. As we advance in this domain, it is imperative to further investigate these proteomic shifts, aiming to mitigate or even reverse the detrimental effects of MHE in the aging population, which involves cognitive and neurological declines.

Acknowledgment

The authors acknowledge Prof. SK Trigun Laboratory facilities, Department of Zoology, BHU, BSBE IIT Bombay for the MASSFIITB facility (BT/PR13114/INF/22/206/2015), Central Discovery Center, BHU for providing Neurolucida and trinocular research microscope facilities, and ISLS, BHU for providing liquid nitrogen. VVS thanks the UGC-Junior Research Fellowship. SKP thanks the Science and Engineering Research Board, Government of India, for the project assistance fellowship and the Indian Council of Medical Research for a senior research fellowship.

Funding

This work was financially supported by an ECRA grant from the Science and Engineering Research Board (ECR/2017/001018), the Government of India, a Start Up Grant (Dev. Scheme No. 5007), and a Research Grant for Faculty (IoE Dev. Scheme-6031) from Banaras Hindu University to the PA.

Conflict of interest

The authors declare that they have no competing interests.

Authors’ contributions

Vishal Vikram Singh: Investigation, Formal analysis, Validation, Visualization, Writing- original draft. Shambhu Kumar Prasad: Investigation, Writing-review, and editing. Arup Acharjee: Conceptualization, Writing-review. Sanjeeva Srivastava: Writing-review. Papia Acharjee: Conceptualization, Funding acquisition, Supervision, Writing-review, and editing.

Data availability

The MS raw data are available from the ProteomeXchange consortium and can be accessed via the dataset identifier PXD054493 (Username: [email protected], Password: MTvYujPdpUsG).

A supplementary figure can be found in the shared supplementary files. Other data related to this work can be shared upon request to the corresponding author.

Ethics Approval

This study was approved by the Institutional Animal Ethical Committee (IAEC), Banaras Hindu University, Varanasi, India.

Consent to participate

Consent to Publication

All the authors reviewed the manuscript and agreed to publish this article.

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Investigating Hippocampal Proteome Dynamics in Aging Rats with Minimal Hepatic Encephalopathy via High-Resolution Mass Spectrometry

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Chemicals

2.2 Experimental animals and housing

2.3 Induction of MHE in old rats and hippocampal tissue collection

2.4 Novel Object Recognition (NOR) Test for Assay Working Memory

2.5 Nissl staining of the hippocampus

2.6 Neuronal arborization of pyramidal neurons in the hippocampal region via Golgi Cox staining

2.7 Sample preparation for the LC‒MS/MS analysis

2.7.1 Protein extraction

2.7.2 Protein digestion

2.8 Label-free LC‒MS/MS analysis

2.9 Proteomic data processing and statistical analysis

2.10 Biological Pathway Enrichment and Interactome Analysis

2.11 Validation of the identified hippocampal proteins

2.11.1 Western blot analysis

2.11.2 Immunofluorescence analysis

2.11.3 Colocalization of Fetuin A, P23, and Intersectin-1 with NeuN and GFAP

2.12 Statistical analysis

3 Results

3.1 Impact of MHE on memory function in aging rats

3.2 Neuro-morphological changes in the hippocampus

3.3 Dendritic arborization of the hippocampal neurons

3.4 Discovery-based proteomics-LC‒MS/MS

3.5 Functional enrichment analysis and protein‒protein interaction

3.6 Validation of the candidate protein via western blotting and spatial distribution via immunofluorescence

3.7 Cell-specific distribution of validated proteins

4 Discussion

5 Limitations of the Study

6 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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