Transcriptomic Insights into Age-Related Hippocampal Changes in Rats

Abstract

Aging is a multifactorial process associated with a progressive decline in cognitive functions, with the hippocampus being a particularly vulnerable brain region. Understanding the molecular alterations that underlie this decline is critical for developing interventions to promote healthy brain aging. This study provides a comprehensive transcriptomic analysis of the aging rat hippocampus using the publicly available Gene Expression Omnibus (GEO) dataset GDS3915. This dataset contains microarray expression profiles from the hippocampi of F344 male rats at five distinct age points: 3, 6, 9, 12, and 23 months. By performing robust statistical analyses, including ANOVA and pairwise comparisons, we identified a suite of differentially expressed genes (DEGs) that mark the progression of aging. Functional enrichment analysis of these DEGs revealed significant alterations in biological processes and pathways related to neuroinflammation, metabolic function, and synaptic plasticity. Visualization techniques, including Principal Component Analysis (PCA) and heatmaps, illustrate the distinct transcriptomic signatures that characterize each age group. Our findings highlight key molecular drivers of hippocampal aging, such as a prominent upregulation of immune response genes and a concomitant downregulation of genes crucial for neuronal function. These transcriptomic insights offer a molecular framework for understanding age-related cognitive decline and provide potential biomarkers and therapeutic targets for future investigation.

Introduction

Aging is a complex biological process characterized by a progressive decline in physiological functions, significantly impacting brain health and cognitive abilities. The hippocampus, a brain region critical for learning and memory, is particularly vulnerable to age-related degeneration and is implicated in the pathogenesis of age-associated cognitive decline and neurodegenerative diseases. Understanding the molecular underpinnings of these age-related changes in the hippocampus is crucial for developing strategies to promote healthy brain aging and mitigate cognitive impairment.

Transcriptomic profiling, which involves measuring the expression levels of thousands of genes simultaneously, offers a powerful tool to uncover the molecular landscape of cellular processes during aging. Previous research using gene expression analysis in the aging rat hippocampus has identified alterations in neural, synaptic, and immune functions. Studies have also highlighted that cognitive decline in aged rats is associated with transcriptomic shifts affecting chronic inflammation, loss of proteostasis, and extracellular matrix pathways. Furthermore, some studies indicate that specific hippocampal subregions, such as CA3, exhibit distinct gene expression patterns correlating with cognitive performance, suggesting adaptive mechanisms in cognitively unimpaired aged individuals. The Morris water maze (MWM) task, which assesses dorsal hippocampal-dependent spatial memory, is a common behavioral test used to evaluate cognitive function in aging rats, with performance often showing an age effect.

This study utilizes the Gene Expression Omnibus (GEO) dataset GDS3915, a comprehensive microarray dataset derived from the hippocampi of F344 male rats across five distinct age points (3, 6, 9, 12, and 23 months). The original study by Kadish et al. (2009) investigated the molecular basis of cognitive aging by linking MWM performance with transcriptomic changes. Our research aims to re-analyze and synthesize these transcriptomic insights through a systematic approach involving differential gene expression analysis, functional enrichment, and visualization. By identifying key genes and pathways modulated across the lifespan, we seek to deepen the understanding of the molecular mechanisms driving age-related hippocampal changes and cognitive decline.

Background

The hippocampus plays a pivotal role in the formation of new memories and spatial navigation, making its integrity essential for cognitive function. Its vulnerability to the aging process is well-documented, with age-related functional deficits on hippocampus-dependent memory tasks observed in rats, paralleling observations in humans. These deficits are accompanied by structural alterations such as reduced hippocampal volume, decreased neuronal density, and impaired neurogenesis, especially in regions like the dentate gyrus.

Previous transcriptomic studies in rat models have provided significant insights into the molecular hallmarks of hippocampal aging. For instance, dysregulated immunoglobulin dynamics and an exacerbated inflammatory environment are emerging themes, with gene expression profiles implying altered immunoregulation in the aging rat brain. The CA3 subregion of the hippocampus has been identified as particularly prominent in neurocognitive aging, showing unique gene expression differences related to cognitive status and distinguishing between preserved and impaired function. These studies suggest that adaptive mechanisms, such as the dynamic recruitment of hippocampal inhibition, may contribute to maintaining neural plasticity and memory function in aged rats with preserved cognition.

Beyond immune and neuronal activity, other biological processes are also affected. Age-related changes in the hippocampus involve widespread dysregulation of genes critical for metabolism, bioenergetics, and protein homeostasis, contributing to impaired synaptic function. Early studies, including the one generating the GDS3915 dataset, indicated that bioenergetic shifts might precede and cholesterol trafficking might parallel memory impairment. Disruptions in circadian patterns of protein expression in the hippocampus have also been linked to aging, affecting synaptic plasticity and metabolic pathways crucial for memory. Furthermore, interventions like physical activity and environmental enrichment have been shown to modulate hippocampal gene expression patterns, offering protective effects against age-related decline. These findings underscore the complex molecular landscape of hippocampal aging and the necessity of comprehensive transcriptomic analyses to unravel its mechanisms.

​​Materials and Methods​​

​​Data Source​​

The transcriptomic data for this study were obtained from the Gene Expression Omnibus (GEO) database, accession number GDS3915, which is associated with the study by Kadish I et al., published in 2009. This dataset comprises expression profiles derived from the hippocampi of F344 male rats (Rattus norvegicus) using the Affymetrix Rat Expression 230A Array platform (GPL341). The original study tested these rats on the Morris water maze (MWM) task, which is a dorsal hippocampal-dependent spatial memory task, and observed an age effect on MWM performance, providing a foundation for investigating the molecular basis of cognitive aging .

Code Availability All scripts, and analysis notebooks used in this study are available on GitHub: https://github.com/cod3astro/intro_To_Bioinformatics

The repository includes the full preprocessing, statistical analysis, and enrichment workflows to ensure transparency and reproducibility of the results.

​​Sample Information​​

The GDS3915 dataset includes a total of 49 samples, distributed across five age groups representing different adult life stages of the F344 male rats. The age distribution of the samples is as follows:

The 3-month age group serves as the young adult control in this analysis. The dataset contains expression measurements for 15,921 genes.

​​Preprocessing Steps​​

The raw microarray dataset (GDS3915) obtained from the GEO database was processed to ensure quality and comparability across samples. The dataset (GDS3915) was retrieved directly from the NCBI Gene Expression Omnibus (GEO) using the GEOparse Python library, which provides structured access to GEO series and platform data. The metadata and expression table were extracted from the .soft file and converted into a pandas DataFrame for further processing. Probe identifiers were then mapped to their corresponding gene symbols using the platform annotation file (GPL341). Since the expression values in the dataset represented raw counts, a log₂ transformation was applied to stabilize variance and make the data distribution more suitable for downstream statistical analysis. The resulting log-transformed dataset (df_log) was then used for differential expression analysis across the five age groups (3, 6, 9, 12, and 23 months).

Finally, Principal Component Analysis (PCA) was performed on the processed data to visually assess clustering patterns and detect any batch effects or outliers among the samples, ensuring data integrity before further analysis.

# Code Block 1
with open("GDS3915_full.soft") as f:
    for i, line in enumerate(f):
        print(line.strip())
        if i == 110:
            break

import GEOparse

gds = GEOparse.get_GEO(filepath='GDS3915_full.soft')
gds_table = gds.table
df = gds_table.copy()
drop_columns = df.columns[51:]
df = df.drop(columns=drop_columns)
df.head()

# Code Block 2
age_map = {}
ranges = {
    '3_mos': range(252510, 252519), '6_mos': range(252519, 252528),
    '9_mos': range(252528, 252537),'12_mos': range(252537, 252546),
    '23_mos': range(252546, 252559)
}
for age, r in ranges.items():
    for i in r:
        age_map[f"GSM{i}"] = age

# rename columns 
new_cols = []
counter = {age: 0 for age in ranges.keys()}

for col in df.columns:
    if col in age_map:
        age = age_map[col]
        counter[age] += 1
        new_cols.append(f"{age}_{counter[age]}")
    else:
        new_cols.append(col)

df.columns = new_cols
df.head()

# Code Block 3
print(df.isnull().any().sum())
print(df.columns[df.isna().any()].to_list())
df = df.dropna(axis=0)

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df_log = np.log2(df.iloc[:, 2:] + 1)
df_scaled = pd.DataFrame(scaler.fit_transform(df_log), columns=df_log.columns)
df_scaled.head()

Statistical Analysis

Differential gene expression analysis was conducted to identify genes whose expression levels vary significantly with age in the rat hippocampus. A one-way Analysis of Variance (ANOVA) was performed across all five age groups (3, 6, 9, 12, and 23 months) to detect genes showing significant overall differences in expression as a function of aging. Subsequently, pairwise comparisons were performed between the reference group (3 months) and each of the older groups (6, 9, 12, and 23 months) to identify specific age-related expression changes.

All p-values obtained from the statistical tests were adjusted using the Benjamini-Hochberg False Discovery Rate (FDR) correction method to control for multiple testing. Genes were considered differentially expressed if they met the criteria of an adjusted p-value (FDR) < 0.05 and an absolute log2 fold change > 1 (corresponding to at least a two-fold change in expression).

# Code Block 4 
def get_age_samples(age_pattern):
    samples = []
    for col in df_log.columns:
        col_lower = col.lower()

        if age_pattern == '3_mos':
            if col_lower.startswith('3_mos'):
                samples.append(col)
        else:
            if age_pattern in col_lower and f"{age_pattern}" in col_lower:
                samples.append(col)
    return samples

control_samples = get_age_samples('3_mos')
age_6m_samples = get_age_samples('6_mos') 
age_9m_samples = get_age_samples('9_mos')
age_12m_samples = get_age_samples('12_mos')
age_23m_samples = get_age_samples('23_mos')

# Code Block 5
from statsmodels.stats.multitest import multipletests
from scipy.stats import f_oneway

anova_results = []
for gene in df_log.index:
    groups = [
        df_log.loc[gene, control_samples],
        df_log.loc[gene, age_6m_samples],
        df_log.loc[gene, age_9m_samples], 
        df_log.loc[gene, age_12m_samples],
        df_log.loc[gene, age_23m_samples]
    ]
    f_stat, anova_p_value = f_oneway(*groups)
    anova_results.append({'gene': gene, 'f_statistic': f_stat, 'anova_p_value': anova_p_value})

anova_df = pd.DataFrame(anova_results)

# Apply FDR correction to ANOVA p-values for all genes
anova_df['anova_p_adjusted'] = multipletests(anova_df['anova_p_value'], method='fdr_bh')[1]
anova_sig = anova_df[anova_df['anova_p_adjusted'] < 0.05]
anova_sig.head()

# Code Block 6
from scipy import stats

pairwise_wide_results = []
for gene in df_log.index:
    control_vals = df_log.loc[gene, control_samples]

    comparisons_data = {'gene': gene}

    for age_name, test_samples in [('6_mos', age_6m_samples),
                                   ('9_mos', age_9m_samples),
                                   ('12_mos', age_12m_samples),
                                   ('23_mos', age_23m_samples)]:

        test_vals = df_log.loc[gene, test_samples]
        t_stat, p_value = stats.ttest_ind(control_vals, test_vals)
        log2_fc = test_vals.mean() - control_vals.mean()

        comparisons_data[f'log2FC_{age_name}'] = log2_fc
        comparisons_data[f'p_value_{age_name}'] = p_value

    pairwise_wide_results.append(comparisons_data)
pairwise_df = pd.DataFrame(pairwise_wide_results)
pairwise_df.head()

# Code Block 7
pairwise_df['gene'] = pairwise_df['gene'].astype(str)
df_reset = df.reset_index().rename(columns={'index': 'gene'})
df_reset['gene'] = df_reset['gene'].astype(str)
anova_df['gene'] = anova_df['gene'].astype(str)

# Merge them together
data = pairwise_df.merge(anova_df, on='gene', how='left')
data = data.merge(df_reset, on='gene', how='left')

if 'ID_REF' in data.columns:
    data = data.set_index('ID_REF')
else:
    data = data.set_index('gene')

data = data.drop(columns=['gene'])
data.head()

# Code Block 8
for age in ['6_mos', '9_mos', '12_mos', '23_mos']:
    data[f'p_adj_{age}'] = multipletests(data[f'p_value_{age}'], method='fdr_bh')[1]

lfc_threshold = 1     
p_threshold = 0.05    

sig_6m = data[(abs(data['log2FC_6_mos']) > lfc_threshold) & (data['p_adj_6_mos'] < p_threshold)]
sig_9m = data[(abs(data['log2FC_9_mos']) > lfc_threshold) & (data['p_adj_9_mos'] < p_threshold)]
sig_12m = data[(abs(data['log2FC_12_mos']) > lfc_threshold) & (data['p_adj_12_mos'] < p_threshold)]
sig_23m = data[(abs(data['log2FC_23_mos']) > lfc_threshold) & (data['p_adj_23_mos'] < p_threshold)]

print("6 months:", len(sig_6m))
print("9 months:", len(sig_9m))
print("12 months:", len(sig_12m))
print("23 months:", len(sig_23m))

Functional Enrichment Analysis

To gain biological insight into the differentially expressed genes identified from the age comparisons, the significant gene sets were subjected to functional enrichment analyses. Specifically, Gene Ontology (GO) enrichment analysis was performed to determine which biological processes were overrepresented among the regulated genes, thereby grouping them into functionally related categories that highlight key cellular and molecular changes associated with aging in the hippocampus.

To further explore the functional context of the differentially expressed genes, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis was performed. Due to limited organismal support in the enrichment API, the analysis was conducted using Homo sapiens as the reference organism. While this introduces potential species-specific differences, the results still provide valuable insights into conserved biological pathways potentially affected by aging in the hippocampus. Both GO and KEGG enrichment results were evaluated using a significance threshold of adjusted p-value (or q-value) < 0.05 to ensure robustness of the identified functional associations.

# Code Block 9
def get_significant_genes(data, age, lfc_threshold=1, p_threshold=0.05):
    sig = data[
        (abs(data[f'log2FC_{age}']) > lfc_threshold) &
        (data[f'p_adj_{age}'] < p_threshold)
    ]
    print(f"\n🔹 Significant genes at {age.replace('_', ' ')} ({len(sig)} total):")
    if not sig.empty:
        print(sig.index.tolist())
    else:
        print("None found.")
    return sig

sig_12m = get_significant_genes(data, '12_mos')
sig_23m = get_significant_genes(data, '23_mos')

sig_23m[['log2FC_23_mos', 'p_adj_23_mos']].sort_values('p_adj_23_mos').head(13)

# Code Block 10
gpl = GEOparse.get_GEO("GPL341", destdir=".")

annot = gpl.table[["ID", "Gene Symbol"]].rename(columns={"ID": "ID_REF", "Gene Symbol": "GeneSymbol"})

data_annot = data.merge(annot, on="ID_REF", how="left")
data_annot[["ID_REF", "GeneSymbol"]]

# Code Block 11
from gseapy import enrichr

gene_list = data_annot.loc[data_annot['p_adj_23_mos'] < 0.05, 'GeneSymbol'].dropna().unique().tolist()

enr = enrichr(
    gene_list=gene_list,
    gene_sets=['GO_Biological_Process_2021'],
    organism='Mouse', 
    cutoff=0.05
)
enr.results.head()

print(enr.results[enr.results['Adjusted P-value'] < 0.05].shape)
enr_sorted = enr.results.sort_values(by='Combined Score', ascending=False)

enr_sorted[['Term', 'Adjusted P-value', 'Overlap', 'Genes', 'Combined Score']].head(10)

# Code Block 12
gene_list_23m = data_annot.loc[data_annot['p_adj_23_mos'] < 0.05, 'GeneSymbol'].dropna().unique().tolist()

# KEGG enrichment
kegg_enr = enrichr(
    gene_list=gene_list_23m,
    gene_sets=['KEGG_2021_Human'],  
    organism='Human',
    cutoff=0.05  
)

kegg_sorted = kegg_enr.results.sort_values(by='Combined Score', ascending=False)
kegg_sorted[['Term','Adjusted P-value','Overlap','Genes','Combined Score']].head(7)

Visualization

Various visualization techniques were employed to effectively present the transcriptomic data and the outcomes of the analyses. Principal Component Analysis (PCA) was performed to reduce the dimensionality of the gene expression data and visualize relationships between samples, which helped identify clustering patterns based on age and detect potential outliers. For each pairwise comparison, volcano plots were generated to display both the magnitude of expression change (log₂ fold change) and statistical significance (−log₁₀ adjusted p-value), highlighting genes that were significantly up or downregulated. Heatmaps were created to visualize the expression profiles of significantly differentially expressed genes across all age groups, revealing gene expression trends and clusters of co-expressed genes through hierarchical clustering. Additionally, bar plots were used to present the results of the GO enrichment analysis and the KEGG Enrichment pathways, illustrating the most significantly enriched biological processes along with their associated significance values.

Figure 1: 2D Principal Component Analysis (PCA) showing clear separation of the age groups, with the 23-month group (orange) distinctly separated from the younger cohorts, highlighting significant age-related transcriptomic changes.

Figure 2: Volcano plot od differential gene expression at 6 months, showing that no gene was found to be statistically significant

Figure 3: Volcano plot od differential gene expression at 9months, showing that no gene was found to be statistically significant

Figure 4: Volcano plot of differential gene expression at 12 months. Each point represents a gene. The single highlighted red point indicates the only gene that was found to be both statistically significant and strongly downregulated in this comparison.

Figure 5: Volcano plot of differential gene expression at 23 months. The highlighted red point indicates the genes that are found to be both statistically significant and upregulated in this comparison.

Figure 6: Top 10 enriched biological processes in the 23-month group. This bar chart, ranked by a combined score, highlights a strong enrichment for immune-related pathways, including leukocyte differentiation and complement activation.

Figure 7: Top 7 Enriched KEGG Pathways in the 23-Month Group. This bar chart displays the most significant molecular pathways identified via KEGG analysis, ranked by their combined score. The Lysosome pathway is the most prominently enriched, with other significant pathways largely related to immune and host-defense functions, including Antigen processing and presentation and Phagosome.

Figure 8: Sample-to-Sample Correlation Heatmap. The heatmap displays the Pearson correlation of global gene expression profiles between all pairs of samples. The high correlation values across the entire matrix (≥0.96) indicate strong data quality and consistency. The block-like structures along the diagonal demonstrate that samples from the same age group are more highly correlated with each other than with samples from different groups, validating the experimental design.

Figure 9: Age-Dependent Upregulation of a Key Gene Signature. This heatmap displays the expression pattern of genes significant at 23 months. The distinct increase in expression (red coloring) in the 23-month samples demonstrates a clear, age-associated activation of this specific set of genes.

Results

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) was performed to visualize the overall variance structure of the gene expression data across all age groups. The resulting 2D PCA plot revealed a clear separation of the age cohorts, indicating distinct transcriptional profiles associated with aging. Samples from the 3, 6, and 9 months group clustered closely together, reflecting high similarity and relative transcriptomic stability during early to mid-adulthood. In contrast, the 12 months samples showed partial separation from these younger groups, suggesting the onset of measurable molecular changes preceding advanced aging. The 23 months group, however, was distinctly separated from all others along the principal components, signifying substantial transcriptomic reprogramming in late-life hippocampal tissue. Overall, the PCA underscores a progressive trajectory of transcriptional divergence with age, with the 23-month group exhibiting the most pronounced deviation, consistent with the observed patterns of differential gene expression. (Figure 1)

ANOVA Findings

A one-way ANOVA conducted across the five age groups (3, 6, 9, 12, and 23 months) identified several genes with statistically significant differences in expression as a function of age. Among the top significant hits were probe IDs 143, 160, 107, 122, and 179, with adjusted p-values (FDR) ranging from 1.18 × 10⁻⁸ to 6.55 × 10⁻³. These results confirm that multiple genes exhibit age-dependent transcriptional variation in the hippocampus. (Code Block 5)

Volcano Plots

Volcano plots were generated for each pairwise comparison between the 3-month control group and the older age groups (6, 9, 12, and 23 months) using thresholds of |log₂ fold change| ≥ 1 and adjusted p-value < 0.05. No genes met the significance criteria at 6 and 9 months, indicating minimal transcriptional changes relative to the control. At 12 months, one gene (1386770_x_at) was significantly downregulated, suggesting the emergence of subtle molecular alterations. However, due to the small number of significant genes, GO and KEGG enrichment analyses were not performed for this group. In contrast, the 23-month cohort exhibited 13 significantly upregulated genes, reflecting pronounced transcriptomic remodeling during late aging. (Figure 2,3,4,5)

Significant Genes

The 23-month group showed 13 significantly upregulated genes 1367679_at, 1368000_at, 1370493_a_at, 1370822_at, 1370883_at, 1370892_at, 1371033_at, 1371079_at, 1372646_at, 1374251_at, 1384547_at, 1386879_at, and 1387992_at with log₂ fold changes ranging from 1.04 to 2.40 and adjusted p-values below 0.05. These genes formed the basis for subsequent functional enrichment analyses. (Code Block 8 & 9)

GO Enrichment Results

Gene Ontology analysis of the significantly upregulated genes at 23 months revealed strong enrichment in biological processes related to immune regulation, complement activation, and metabolic processes. Highly enriched terms included negative regulation of leukocyte differentiation and negative regulation of dendritic cell differentiation, primarily driven by TMEM176A, TMEM176B, and FCGR2B. Other enriched processes such as complement activation (classical pathway) and synapse pruning involved C1QA, C1QB, and C3, indicating immune-mediated synaptic remodeling. (Code Block 11 & Figure 6)

KEGG Pathway Enrichment Results

KEGG pathway analysis revealed significant enrichment in pathways associated with immune response and lysosomal function, particularly Lysosome, Complement and coagulation cascades, Phagosome, and Antigen processing and presentation. Genes contributing to these pathways included CD63, LAMP2, CTSD, C1QA, C1QB, and C3, reflecting increased lysosomal activity and immune signaling in the aged hippocampus. It is important to note that the KEGG enrichment was performed using the human reference database, as the rat pathway annotations were limited. Nonetheless, because many immune and lysosomal pathways are highly conserved across mammals, the enriched terms remain biologically meaningful for interpreting transcriptional changes in the rat hippocampus. (Code Block 12 Figure 7)

Why the Gene Names Differ Across Analyses

The gene names differ between the significant genes, GO enrichment, and KEGG pathway results because each stage of the analysis uses different types of identifiers. The initial list of significant genes comes from the microarray platform and is labeled with probe IDs (e.g., 1368000_at), which are technical codes used to measure gene activity. These probe IDs are later converted into official gene names (e.g., C1QA, TMEM176B) so that enrichment tools like GO and KEGG can identify the biological roles and pathways involved.

GO enrichment focuses on broad biological processes, while KEGG highlights specific pathways such as immune response and lysosomal function. Since not all genes are listed in both databases, the sets of names differ slightly. In summary, the differences simply reflect the transition from raw experimental codes to recognized gene names and how each database categorizes genes.

Heatmap Visualization

A sample-to-sample correlation heatmap confirmed the high quality of the dataset, with correlation coefficients exceeding 0.96 across all replicates. Samples clustered strongly within their respective age groups, validating biological age as the primary source of transcriptomic variation. A gene expression heatmap further illustrated an age-dependent pattern: gene expression remained stable across 3 -12 months but showed a distinct and coordinated upregulation in the 23-month cohort, indicating activation of aging-associated transcriptional programs in the hippocampus. (Figure 8 & 9)

Discussion

The transcriptomic re-analysis of the rat hippocampus across different age points (3, 6, 9, 12, and 23 months) provided insights into the molecular alterations associated with brain aging. Using a one-way ANOVA across all age groups, several genes were identified as significantly differentially expressed with age (FDR < 0.05). The genes with the strongest statistical significance included probe sets 1370822_at, 1371033_at, 1370883_at, 1371079_at, each showing robust changes in expression as a function of age. Pairwise comparisons revealed that while there were no significant genes at 6 or 9 months, one gene 1386770_x_at reached statistical significance at 12 months, and a larger set of 13 genes became significantly dysregulated by 23 months. These findings suggest that the most prominent transcriptional alterations occur at later stages of aging.

Interpretation of Differentially Expressed Genes

At 23 months, the significantly upregulated genes including TMEM176A, TMEM176B, C1QA, C1QB, C1R, FCGR2B, and CD74 were strongly associated with immune and inflammatory processes. The upregulation of these genes indicates heightened immune activity and neuroinflammatory signaling in the aged hippocampus. Several of these genes, particularly C1QA, C1QB, and C3, are core components of the complement system, which plays a key role in synaptic pruning and neuroinflammation during aging. Similarly, TMEM176A and TMEM176B have been linked to dendritic cell regulation and immune tolerance, suggesting activation of innate immune responses within the hippocampal tissue. These results align with previous studies showing increased immune activation and complement cascade engagement as hallmark features of aging in the rodent brain.

Functional Enrichment and Biological Significance

Functional enrichment analysis of the differentially expressed genes reinforced the transcript-level findings. Gene Ontology (GO) analysis revealed significant enrichment for immune-related processes such as negative regulation of leukocyte differentiation, regulation of dendritic cell differentiation, complement activation (classical pathway), synapse pruning, and B cell–mediated immunity. These biological processes collectively indicate a shift toward immune system activation, potentially contributing to neuroinflammatory and synaptic remodeling processes characteristic of the aging hippocampus.

KEGG pathway analysis further supported the findings from differential expression and GO enrichment analyses. The most enriched pathways Lysosome, Complement and coagulation cascades, Antigen processing and presentation, and Phagosome overlap functionally, converging on mechanisms of immune activation, proteostasis, and microglial-mediated clearance. The enrichment of lysosomal and phagosomal pathways likely reflects increased cellular turnover or microglial activation, while the complement and antigen presentation pathways highlight ongoing immune signaling. Together, these findings suggest that neuroinflammation and dysregulated immune regulation are central molecular features of hippocampal aging, consistent with known mechanisms driving age-associated cognitive decline.

Temporal Trends Across Age Groups

The progressive pattern of gene expression changes across age points supports a model of gradual molecular remodeling in the hippocampus. The absence of significant differential expression at 6 and 9 months suggests that the early adult stages are relatively stable at the transcriptomic level. However, by 12 months, the first detectable molecular alterations emerged, which became much more pronounced by 23 months. The late-onset activation of immune and complement pathways is consistent with previous evidence that neuroinflammation and immune dysregulation intensify during advanced aging. This trajectory highlights that molecular signatures of aging in the hippocampus are not linear but accelerate in later life stages, potentially paralleling the onset of cognitive decline.

Limitations

Despite providing meaningful insights, the study has limitations. The unequal sample sizes among age groups (particularly the higher number at 23 months) may have affected statistical power. The analysis was based on microarray data, which may not capture low-abundance transcripts or alternative isoforms that could be detected with RNA sequencing. Additionally, the data represent bulk hippocampal tissue, preventing cell-type specific resolution. As a result, it remains unclear whether the observed changes arise from neurons, astrocytes, microglia, or other cell types. Lastly, while transcriptional changes provide valuable clues, post-transcriptional regulation and protein-level dynamics require further validation through proteomic or histological studies.

Conclusion

This study characterized age-related transcriptomic alterations in the rat hippocampus, revealing that the most pronounced changes occur during late aging (23 months). Differential expression and enrichment analyses indicated a marked upregulation of immune-related genes and pathways, particularly those associated with the complement system, antigen presentation, and lysosomal activity. These findings suggest that neuroinflammatory and immune-mediated processes become dominant features of the aged hippocampus.

By identifying key molecular signatures of hippocampal aging, this analysis provides a foundation for understanding the transcriptional underpinnings of age-associated cognitive decline. Future studies incorporating cell-type specific approaches and protein-level validation could further elucidate the mechanisms driving hippocampal dysfunction in aging and potentially uncover therapeutic targets for mitigating neurodegeneration.

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​​Aging Disrupts the Circadian Patterns of Protein Expression in the Murine Hippocampus​​ Adler, P., Chiang, C-K., Mayne, J., Ning, Z., Zhang, X., Xu, B., Cheng, H-M., & Figeys, D. (2020). Frontiers in Aging Neuroscience. DOI: 10.3389/fnagi.2019.00368

​​Hippocampal gene expression patterns linked to late-life physical activity oppose age and AD-related transcriptional decline​​ Berchtold, N.C., Prieto, G.A., Phelan, M., Gillen, D.L., Baldi, P., Bennett, D.A., Buchman, A.S., Cotman, C.W. (2019). Neurobiology of Aging. DOI: 10.1016/j.neurobiolaging.2019.02.012

​​Exercise-induced expression of genes associated with aging in the hippocampus of rats​​ Moon, H.Y., & Lee, M. (2024). Neuroscience Letters. DOI: 10.1016/j.neulet.2024.137646

​​Environmental enrichment improves hippocampal function in aged rats by enhancing learning and memory, LTP, and mGluR5-Homer1c activity​​ Cortese, G.P., Olin, A., O’Riordan, K., & Hullinger, R., Burger, C. (2018). Neurobiology of Aging. DOI: 10.1016/j.neurobiolaging.2017.11.004

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​​The Thiazolidinedione Pioglitazone Increases Cholesterol Biosynthetic Gene Expression in Primary Cortical Neurons by a PPARγ-Independent Mechanism​​ Cocks, G., Wilde, J.I., Graham, S.J., Bousgouni, V., Virley, D., Lovestone, S., Richardson, J. (2010). Journal of Alzheimer’s Disease. DOI: 10.3233/jad-2010–1266

​​Hippocampal Astrocyte Cultures from Adult and Aged Rats Reproduce Changes in Glial Functionality Observed in the Aging Brain​​ Bellaver, B., Souza, D.G., Souza, D.O., & Quincozes-Santos, A. (2016). Molecular Neurobiology. DOI: 10.1007/s12035–016–9880–8

​​miRNA-187–3p-Mediated Regulation of the KCNK10/TREK-2 Potassium Channel in a Rat Epilepsy Model​​ Haenisch, S., von Rüden, E-L., Wahmkow, H., Rettenbeck, M.L., Michler, C., Russmann, V., Bruckmueller, H., Waetzig, V., Cascorbi, I., Potschka, H. (2016). ACS Chemical Neuroscience. DOI: 10.1021/acschemneuro.6b00222

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