Abstract
Aging is a process that leads to the deterioration in physiological functioning of the brain. Prior research has proposed that hippocampal aging is accompanied by genetic alterations in neural, synaptic, and immune functions. Nevertheless, interactome-based interrogations of gene alterations in hippocampal aging, remain scarce. Our study integrated gene expression profiles of the hippocampus from young and aged rats and functionally classified network-mapped genes based on their interactome. Hippocampal differentially expressed genes (DEGs) between young (5–8 months) and aged (21–26 months) male rats (Rattus norvegicus) were retrieved from five publicly available datasets (GSE14505, GSE20219, GSE14723, GSE14724, and GSE14725; 38 young and 29 aged samples). Encoded hippocampal proteins of age-related DEGs and their interactome were predicted. Clustered network DEGs were identified and the highest-ranked was functionally annotated. A single cluster of 19 age-related hippocampal DEGs was revealed, which was linked with immune response (biological process, P = 1.71E-17), immunoglobulin G binding (molecular function, P = 1.92E-08), and intrinsic component of plasma membrane (cellular component, P = 1.25E-06). Our findings revealed dysregulated hippocampal immunoglobulin dynamics in the aging rat brain. Whether a consequence of neurovascular perturbations and dysregulated blood-brain barrier permeability, the role of hippocampal immunoregulation in the pathobiology of aging warrants further investigation.
Introduction
Aging is a ubiquitous yet inevitable biological phenomenon that drives the transient deterioration in physiological functioning of the brain. Brain aging is associated with cognitive decline including memory and executive function, perturbations which often correlate with age-driven structural alterations that are accompanied by neuronal loss and synaptic dysfunction (). Aging also constitutes the primary risk factor for many neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease, of which prevalence increases with advancing age ().
The hippocampus is a brain locus fundamental to cognition, with a profound role in learning and memory consolidation (). Prior research has documented that structural and functional changes in the hippocampus correlate with development and disease severity of neurodegenerative disorders linked with cognitive decline. Previous studies have revealed that hippocampal aging is similar in humans and in animal models, with prominent genetic dysregulation in neural, synaptic and immune functions.
Neurobiological alterations seen in the aging brain can precede apparent histopathological degeneration of the brain, suggesting that analysis of brain gene expression may offer unique insights into the molecular mechanisms underlying age-related changes (). At present, interactome mapping and network-based interrogations of gene-specific targets bearing functional significance in hippocampal aging, remain largely unexplored. In this study, we integrated publicly available gene expression profiles of the hippocampus from young and aged rats to identify putative alterations in hippocampal processes underlying aging.
Materials and Methods
Collection of Microarray Data
We screened the literature through the Gene Expression Omnibus for relevant peer-reviewed datasets based on organism type (Rattus norvegicus), expression profiling (microarray), sample type (brain hippocampal tissue), and condition (aging), using the search terms “aged” or “aging” or “old” or “young” and “brain” or “hippocampus.” No restrictions based on language and geographic origin were used in our search, while no exclusion criteria in the baseline characteristics of animals from which tissue sections were obtained, were applied. Duplicate gene expression sample and series or those lacking expression data for controls or with incompatible platforms, were excluded. Author (PG) formulated the search strategy and in conjunction with a second author (KP), the yielded datasets were screened and any discrepancies in the literature search were resolved.
Identification of Differentially Expressed Genes
Brain hippocampal samples from healthy young and aged rat models were compared and differentially expressed genes (DEGs) were retrieved using ImaGEO (). Significance analysis and integration were conducted using the fixed effect model to identify those DEGs with the strongest average effect across the collected datasets. This approach was followed as both the treatment (i.e., aging) and expression array platforms were orthogonal between datasets. Genes with a P < 0.05 corrected by the Benjamini-Hochberg (BH) false discovery rate (FDR) were regarded as significant. DEGs with Z score > 1.96 were classified as upregulated, while those with Z score < 1.96 as downregulated (corresponding to a significance level of 5%).
Prediction of Network-Based Protein Interactions
Encoded hippocampal proteins of the aging DEGs and their interactome were predicted into a protein-protein interaction (PPI) network using The Search Tool for the Retrieval of Interacting Genes via a medium probabilistic confidence score of >0.4 and mapped with Cytoscape (). A reasonably moderate cut-off score was ensued to amplify the coverage of all potential protein interactions without inflating their precision. Proteins lacking any interactions were excluded from the network.
Identification of Clustering Modules and Characterization of Gene Hubs
Central gene elements of the aging rat hippocampal network were inferred by measuring network features from their complex interactome and by identifying sub-networks and hub objects (,, ). Highly clustered DEGs or densely connected modules in the PPI network were identified using the molecular complex detection (). Cut-off selection was ensued based on the default network scoring parameters: degree cut-off = 2, haircut cluster finding, node score cut-off = 0.2, K-core = 2, and max depth = 100. The interactome interference of module DEGs in the PPI network was quantified using CytoHubba (). Module DEGs were ranked based on the intersection of 11 established topological algorithms, as previously described: Degree, Closeness, Betweenness, Radiality, Stress, EcCentricity, BottleNeck, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, and Maximal Clique Centrality.
Functional Annotation Enrichment Analysis
Enrichment of aging DEGs in the rat hippocampus was predicted using Gene Ontology (GO) annotations from the Molecular Signatures Database (). Functional classification was ensued using a probability density P < 0.05 following BH FDR correction and categorized into three groups of GO terms: biological process (BP), molecular function (MF), and cellular component (CC).
Results
Overview of Microarray Datasets
The search of the GEO database resulted in 4811 datasets, of which 3981 contained gene expression data from organisms other than rats. From these, 111 microarray datasets were retrieved after exclusion of 717 datasets which included non-profiling by array expression data from samples other than tissues. Further exclusion of 106 studies with either duplicate gene expression samples or series and incompatible gene expression platform resulted in 5 eligible datasets: GSE14505 (), GSE20219 (), GSE14723 (), GSE14724 (), and GSE14725 (; Figure 1). The retrieved datasets consisted of 38 young (5–8 months) and 29 aged (21–26 months) male rat hippocampal tissue samples (Supplementary Table 1).
FIGURE 1
Differentially Expressed Genes in Hippocampal Aging
Integration analysis revealed a total of 527 aging DEGs, of which 278 showed significantly increased hippocampal expression while 249 DEGs decreased expression in the rat brain (Supplementary Table 2).
Protein-Protein Interaction Network and Functional Modules in Hippocampal Aging
A PPI network of DEGs in the aging rat hippocampus was constructed and consisted of a total of 474 encoded proteins with 1,862 interactions. A single highest-ranked clustering module was retrieved and consisted of 19 upregulated aging DEGs with 362 interactions among 92 genes: allograft inflammatory factor 1 (AIF1), complement C1q A chain (C1QA), complement C1q B chain (C1QB), complement C1q C chain (C1QC), CD53 molecule (CD53), CD74 molecule (CD74), colony stimulating factor 1 receptor (CSF1R), cathepsin S (CTSS), Fc epsilon receptor Ig (FCER1G), Fc gamma receptor IIa (FCGR2A), Fc gamma receptor IIb (FCGR2B), Fc gamma receptor IIIa (FCGR3A), fibrinogen like 2 (FGL2), integrin subunit beta 2 (ITGB2), lysosomal protein transmembrane 5 (LAPTM5), macrophage expressed 1 (MPEG1), pleckstrin (PLEK), triggering receptor expressed on myeloid cells 2 (TREM2), and transmembrane immune signaling adaptor TYROBP (TYROBP; Figure 2, Table 1, and Supplementary Table 3). Multi-algorithmic topological analysis revealed MPEG1 as the highest ranked hub gene (Supplementary Table 4). DEGs in the highest-ranked clustering module were most enriched with immune response (BP, P = 1.71E-17), immunoglobulin G binding (MF, P = 1.92E-08), and intrinsic component of plasma membrane (CC, P = 1.25E-06; Supplementary Table 5).
FIGURE 2
TABLE 1
| Gene ID | P-value | Z-score | Gene name |
| AIF1 | 1.32E-02 | 3.64 | Allograft inflammatory factor 1 |
| C1QA | 3.47E-09 | 7.00 | Complement C1q A chain |
| C1QB | 5.43E-04 | 4.60 | Complement C1q B chain |
| C1QC | 2.90E-06 | 5.79 | Complement C1q C chain |
| CD53 | 3.98E-07 | 6.15 | CD53 molecule |
| CD74 | 1.36E-08 | 6.75 | CD74 molecule |
| CSF1R | 4.79E-03 | 3.98 | Colony stimulating factor 1 receptor |
| CTSS | 8.61E-09 | 6.83 | Cathepsin S |
| FCER1G | 4.80E-10 | 7.34 | Fc epsilon receptor Ig |
| FCGR2A | 2.85E-02 | 3.35 | Fc gamma receptor IIa |
| FCGR2B | 4.78E-09 | 6.93 | Fc gamma receptor IIb |
| FCGR3A | 3.36E-02 | 3.29 | Fc gamma receptor IIIa |
| FGL2 | 1.25E-05 | 5.46 | Fibrinogen like 2 |
| ITGB2 | 2.48E-03 | 4.18 | Integrin subunit beta 2 |
| LAPTM5 | 3.70E-06 | 5.73 | Lysosomal protein transmembrane 5 |
| MPEG1 | 5.75E-04 | 4.58 | Macrophage expressed 1 |
| PLEK | 2.67E-03 | 4.16 | Pleckstrin |
| TREM2 | 7.34E-04 | 4.52 | Triggering receptor expressed on myeloid cells 2 |
| TYROBP | 9.92E-09 | 6.80 | Transmembrane immune signaling adaptor TYROBP |
Characteristics of the highest-ranked clustering gene module in the protein-protein interaction network of differentially expressed genes of the hippocampus between young (5–8 months) and aged (21–26 months) rats.
Discussion
Analysis of DEGs from hippocampal tissues from young (5–8 months) and aged (21–26 months) rats, identified a single highly clustered gene module consisted of 19 upregulated DEGs: AIF1, C1QA, C1QB, C1QC, CD53, CD74, CSF1R, CTSS, FCER1G, FCGR2A, FCGR2B, FCGR3A, FGL2, ITGB2, LAPTM5, MPEG1, PLEK, TREM2, and TYROBP. Functional annotations of these DEGs revealed an association with ontology processes of immune response and immunoglobulin G binding, and properties of an intrinsic component of plasma membrane. Our findings revealed dysregulated hippocampal immunoglobulin dynamics both in terms of expression and interactions in the aging rat brain, rendering these DEGs as a potential marker of putative neurogenetic alterations during aging.
Dysfunctions of the blood brain barrier (BBB) are often linked with increased peripheral uptake of immunoglobulins and other plasma proteins in the brain (). Post-mortem insights have demonstrated that breakdown of BBB in response to neurodegeneration is characterized by accumulation in hippocampal blood-derived immunoglobulins. Dysregulated BBB permeability is characterized by perturbations in the crosstalk among brain endothelial cells, pericytes, and astrocytes of the neurovascular microenvironment (). Aging related neurovascular unit dysfunction may arise from normal cell senescence or pathologically under age-related distress, such as cerebrovascular accident or reperfusion injury (i.e., ischemic stroke) and predisposition to neurogenetic-driven degeneration (i.e., Parkinson’s disease and Alzheimer’s disease), with peripheral immune penetration leading disease progression (; ).
Genomic stability is an essential requirement for the maintenance of functional and structural integrity of the BBB. Accumulation of DNA damage in terms of DNA breaks, cross-links, and bases mismatches (; ) and aberrations across the DNA repair machinery (; ; ), have all been described during aging (). In this way, altered DNA damage responses and repair signaling likely underlie the penetration of blood-derived substances and the dysregulation of immunity in the aging hippocampus, both of which are intimately linked with neurodegenerative progression (). Whether a consequence of neurovascular dysfunction and dysregulated blood-brain barrier permeability, the role of hippocampal immunoregulation in the pathobiology of aging warrants further investigation.
Strengths and Limitations
This is the first study that comprehensively examined the potential role of DEGs and their interactome as gene biomarkers in hippocampal aging using 5 publicly available datasets with almost 70 included rat samples and using a multi-algorithmic protein-interaction based functional approach that was dependent on different levels of annotation. Nevertheless, certain conceptual and methodological limitations exist. Hippocampal tissue distribution from which gene expression was derived in the included studies was sparsely heterogeneous, ranging from whole hippocampus samples to those from dorsal and ventral areas, the CA1/CA3 regions or the dentate gyrus. Although histologically distinguishable subfields, these likely share common genomic organizations and thus, aging related accumulation of DNA damage from these subregions may be overlapping (). Moreover, despite that the expression profiling platforms employed by the included datasets were indifferent, lab effects due to experimental variation are known to hinder statistical power in DEGs detection, a challenge which remains even after normalization (; ; ; ; ). Yet, an attempt to address unknown cross-study heterogeneity was made by integrating gene expression rather than considering their overlap between datasets.
Conclusion
Immunoglobulin dysregulation distinguishes hippocampal gene expression between aged and young rat brains. Increased awareness of the potential role of hippocampal-focused immunity in the pathobiology of aging, may reveal whether immunoglobulin dysregulation forms the culprit of hippocampal aging and not merely an epiphenomenon of aging-related alterations in BBB.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Statements
Data availability statement
Publicly available datasets were analyzed in this study. These are available in the Gene Expression Omnibus repository, under the following accession numbers: GSE14505, GSE20219, GSE14723, GSE14724, and GSE1472.
Author contributions
PG conceived and designed the study. PG and KP acquired, collated, analyzed the data, and drafted and revised the manuscript critically for important intellectual content. Both authors gave final approval of the version to be published and have contributed to the manuscript.
Acknowledgments
We thank the Imperial Open Access Fund for funding the article processing charges of this manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2022.915907/full#supplementary-material
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Summary
Keywords
hippocampus, aging, rat brain, differentially expressed genes, gene expression, immunoglobulins, blood-brain barrier
Citation
Giannos P and Prokopidis K (2022) Gene Expression Profiles of the Aging Rat Hippocampus Imply Altered Immunoglobulin Dynamics. Front. Neurosci. 16:915907. doi: 10.3389/fnins.2022.915907
Received
08 April 2022
Accepted
03 May 2022
Published
25 May 2022
Volume
16 - 2022
Edited by
Francesco Napolitano, University of Sannio, Italy
Reviewed by
Ashutosh Kumar, All India Institute of Medical Sciences Patna, India; Binglin Zhu, University at Buffalo, United States
Updates
Copyright
© 2022 Giannos and Prokopidis.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Panagiotis Giannos, panagiotis.giannos19@imperial.ac.uk
This article was submitted to Neurogenomics, a section of the journal Frontiers in Neuroscience
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.