November 2010
Volume 51, Issue 11
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Retina  |   November 2010
Immune Activation in Retinal Aging: A Gene Expression Study
Author Affiliations & Notes
  • Mei Chen
    From the Centre for Vision and Vascular Science, School of Medicine, Dentistry, and Biomedical Sciences, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom; and
  • Elizabeth Muckersie
    the Immunology and Infection Section, Division of Applied Medicine, School of Medicine and Dentistry, University of Aberdeen, Aberdeen, Scotland, United Kingdom.
  • John V. Forrester
    the Immunology and Infection Section, Division of Applied Medicine, School of Medicine and Dentistry, University of Aberdeen, Aberdeen, Scotland, United Kingdom.
  • Heping Xu
    From the Centre for Vision and Vascular Science, School of Medicine, Dentistry, and Biomedical Sciences, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom; and
  • Corresponding author: Heping Xu, Centre for Vision and Vascular Science, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Grosvenor Road, Belfast, BT12 6BA, UK; heping.xu@qub.ac.uk
Investigative Ophthalmology & Visual Science November 2010, Vol.51, 5888-5896. doi:10.1167/iovs.09-5103
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      Mei Chen, Elizabeth Muckersie, John V. Forrester, Heping Xu; Immune Activation in Retinal Aging: A Gene Expression Study. Invest. Ophthalmol. Vis. Sci. 2010;51(11):5888-5896. doi: 10.1167/iovs.09-5103.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: To investigate changes in gene expression during aging of the retina in the mouse.

Methods.: Total RNA was extracted from the neuroretina of young (3-month-old) and old (20-month-old) mice and processed for microarray analysis. Age-related, differentially expressed genes were assessed by the empiric Bayes shrinkage-moderated t-statistics method. Statistical significance was based on dual criteria of a ratio of change in gene expression >2 and a P < 0.01. Differential expression in 11 selected genes was further verified by real-time PCR. Functional pathways involved in retinal aging were analyzed by an online software package (DAVID-2008) in differentially expressed gene lists. Age-related changes in differential expression in the identified retinal molecular pathways were further confirmed by immunohistochemical staining of retinal flat mounts and retinal cryosections.

Results.: With aging of the retina, 298 genes were upregulated and 137 genes were downregulated. Functional annotation showed that genes linked to immune responses (Ir genes) and to tissue stress/injury responses (TS/I genes) were most likely to be modified by aging. The Ir genes affected included those regulating leukocyte activation, chemotaxis, endocytosis, complement activation, phagocytosis, and myeloid cell differentiation, most of which were upregulated, with only a few downregulated. Increased microglial and complement activation in the aging retina was further confirmed by confocal microscopy of retinal tissues. The most strongly upregulated gene was the calcitonin receptor (Calcr; >40-fold in old versus young mice).

Conclusions.: The results suggest that retinal aging is accompanied by activation of gene sets, which are involved in local inflammatory responses. A modified form of low-grade chronic inflammation (para-inflammation) characterizes these aging changes and involves mainly the innate immune system. The marked upregulation of Calcr in aging mice most likely reflects this chronic inflammatory/stress response, since calcitonin is a known systemic biomarker of inflammation/sepsis.

In the Wikipedia English dictionary “aging” is defined as “the accumulation of changes in an organism or object over time.” In humans, biological aging is characterized by a declining ability to respond to stress, increased homeostatic imbalance, and increased risk of disease. In the retina, several neurodegenerative diseases may occur as a result of aging, including primary open angle glaucoma, diabetic retinopathy (in particular, type-2 diabetes) and age-related macular degeneration. Although different cellular and molecular mechanisms may be involved in different types of disease, age is the common contributor to all aforementioned diseases. It is well known that certain genetic and environmental factors may predispose individuals to glaucoma, 1 diabetic retinopathy, 2 or age-related macular degeneration. 3,4 We hypothesize that age-related changes in the retina may provide a background that increases susceptibility to the development of these diseases. According to Harman's “free radical of aging” theory, 5 aging is caused by the accumulation of free radical damage over time in cells and tissues. Under normal physiological conditions, damaged cells are homeostatically removed and replaced by healthy cells. The host immune system provides this scavenging and repair role, thus restoring tissue functionality, and this process has been described as a form of physiological inflammation (para-inflammation). 6,7 However, the system is not foolproof and with increasing age, an imbalance in tissue homeostasis may occur either as a result of increased stress or decreased function in the repair mechanism, which may ultimately lead to pathologic changes that may manifest as, for instance, age-related macular degeneration. 
To investigate the age-related changes in the retina that may place individuals at risk of developing age-related retinal diseases, we used microarray analysis to compare the gene expression profiles of the neuroretina from aged mice (20 months) with those from young (3 months) mice. In this analysis, the genes were grouped in clusters depending on the biological molecular pathways involved. Of interest, the two gene clusters most involved in aging were those implicating the immune-response (Ir) and the defense (tissue stress/injury, TS/I)-response pathways. Two Ir gene subclusters—the complement activation and microglial activation genes—were further verified by immunohistochemical examination of retinal flatmounts and cryosections by confocal microscopy. The strongest gene upregulated was the calcitonin receptor (Calcr), a widely distributed cell surface receptor that binds calcitonin. 8 10 Calcitonin is a recognized acute-phase protein, used as a biomarker for inflammation, including autoimmunity and sepsis. 11,12  
These data confirm the importance of aging as a mediator of the slow activation of Ir and TS/I genes and the likely role of modified inflammatory mechanisms in the development of age-related retinal diseases. It is probable that the magnitude of these changes will correlate with the risk of development of diseases such as macular degeneration. We consider this to be a form of exaggerated or modified para-inflammation. 
Materials and Methods
Animals
All experiments were conducted according to the regulations of the Animal License Act (UK) and to the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. Male C57BL/6 mice were supplied by the Department of Medical Research Facility, University of Aberdeen. Two groups of mice (3-month-old and 20-month-old) were used for gene array analysis and for real-time RT-PCR experiments. For each experiment, six mice were included in each group. The mice were killed by CO2 inhalation. The eyes were enucleated and dissected immediately. Neural retinal tissues were dissected from the RPE/choroidal tissues, snap frozen in liquid nitrogen, and stored at −80°C before RNA extraction. 
RNA Extraction and Microarray
RNA isolation and microarray assay were performed by Miltenyi Biotec (Bergisch Gladbach, Germany). Briefly, on receiving the samples, retinal tissues were processed for total RNA extraction (Trizol; Invitrogen, Carlsbad, CA) according to a standard RNA-extraction protocol. All RNA samples underwent quality control analysis (2100 Bioanalyzer; Agilent Technologies, Santa Clara, CA). Equal amounts of total RNA from the retinas of three mice from the same group were pooled. Since there were six mice in each group this procedure yielded duplicate samples for microarray analysis at each group. The pooled RNA samples were processed for further RNA amplification and labeling (Low RNA Input Linear Amp Kit; Agilent Technologies). Cy3-labeled fragmented cRNA samples were hybridized (Whole Mouse Genome Oligo Microarrays 4 × 44K; Agilent Technologies). Fluorescence signals were then scanned (Microarray Scanner System; Agilent Technologies). 
Data Analysis
The system software (Feature Extraction Software [FES], Agilent Technologies) was used to read out and process the microarray image files. The software determines feature intensities (including background subtraction), rejects outliers, and calculates statistical confidences. The output data were then shipped back to our laboratory for further analysis. 
The statistics package Limma 13 was used to generate normalized data. Differentially expressed genes were assessed by the empiric Bayes shrinkage moderated t-statistics method as part of the Limma Bioconductor package. 13 Genes with a change ratio >2 and regular P < 0.01 were considered as significantly differently expressed. 
The Web-accessible program, Database for Annotation, Visualization and Integrated Discovery (DAVID) 2008 14,15 was used to generate biologically relevant groups of genes from the microarray data. This online based software classifies the genes into three functional groups: biological process, molecular function and cellular component. In this study, the functional annotation clustering tool of DAVID-2008 was used to group the Gene Ontology (GO) terms (i.e., clusters that have similar biological meaning due to sharing similar gene members). Increases in expression reaching P ≤ 0.05 were considered strongly enriched in the annotation categories. Software GenMAPP 16,17 was used to visualize gene expression representing biological pathways and groupings of genes. 
Real-time RT-PCR
In a separate experiment, six mice from each experimental group (3-month and 20-month age groups) were killed, and retinal samples were collected. Total retinal RNA was isolated (RNeasy Mini Kit; Qiagen, Crawley, UK). The same amount of total RNA from each retina was reverse-transcribed into cDNA (SuperScript II Reverse Transcriptase; Invitrogen, Paisley, UK) according the manufacture's instruction (sample size for each group, n = 6). The cDNA then served as a template for real-time RT-PCR. All real-time RT-PCRs were performed in 96-well plates (LightCycler 480 system; Roche Applied Science, Burgess Hill, UK). A gene expression assay (Taqman; Applied Biosystems, Inc.[ABI], Warrington, UK) or SYBR green methods were used whenever appropriate. Primers used with Universal Probe Library (UPL) probes were designed using the Roche Applied Science (Burgess Hill, UK) web assay design center (www.universalprobelibrary.com). All primers were ordered from Sigma-Aldrich (Dorset, UK) and FAM-labeled UPL probes were ordered from Roche Diagnostics Ltd. The Ccl2 gene expression assay kit was supplied by Applied Biosystems. The master mixes (LightCycler 480 SYBR Green I Master and Probes Master) were both obtained from Roche Diagnostics, Ltd). The sequence of primers and probe numbers are listed in Table 1. Mouse GAPDH conjugated with fluorescence dye VIC (Applied Biosystems, Inc.) was used as reference gene. A previous study has shown that aging does not affect tissue GAPDH gene expression. 18 The SYBR green method was used to detect the expression of C3 and Calcr. A gene express assay (Taqman; ABI) was used to detect the expression levels of other genes. Student's t-test was used for all comparisons, and differences at P < 0.05 were statistically significant. Eleven genes were selected for qPCR validation. In each experiment, all samples were tested in triplicate, and the whole experiment was performed twice. 
Table 1.
 
Primer Sequences and Probe Numbers Used for qPCR Study
Table 1.
 
Primer Sequences and Probe Numbers Used for qPCR Study
Primer Name Primer Sequence (5′–3′) Probe No.*
Ms4a6b
    Forward TGC CGC CAA GTC TGT TCT 109
    Reverse CAG GCC AAG TTC CAA CAT AGT
Cdc25c
    Forward GGA AAC ACC CGG ATC TGA A 26
    Reverse ACT TTC CAG ACA GCA AAG CAG
Cyp2e1
    Forward GTCTCC TCA TAG AGA TGG AGA AGG 76
    Reverse AGT CAC AGA AAT ATT TTC CAT TGT GT
Agtr1a
    Forward ACTCACAGCAACCCTCCAAG 9
    Reverse CTCAGACACTGTTCAAAATGCAC
Erg1
    Forward CCCTATGAGCACCTGACCAC 22
    Reverse TCGTTTGGCTGGGATAACTC
Cfb
    Forward CTCGAACCTGCAGATCCAC 1
    Reverse TCAAAGTCCTGCGGTCGT
Cd200r
    Forward AAGAATCAAACAACACAGAACAACA 82
    Reverse CTGTACAGACACTGTAGTGTTCACTTG
Icam1
    Forward CCCACGCTACCTCTGCTC 81
    Reverse GATGGATACCTGAGCATCACC
C3
    Forward AGCAGGTCATCAAGTCAGGC
    Reverse GATGTAGCTGGTGTTGGGCT
Calcr
    Forward GCCTCCCCATTTACATCTGC
    Reverse CTCCTCGCCTTCGTTGTTG
Ccl2 Assay kit for ccl2 (Taqman; Applied Biosystems)
Immunofluorescence Staining
Cryosections of mouse eyes were immunostained with appropriate antibodies and examined by confocal microscopy according to previously described methods. 19,20 Briefly, after 2% paraformaldehyde fixation, the samples were blocked in 5% BSA for 30 minutes and then incubated with biotinylated anti-mouse C3d (1:100, R&D Systems, Inc., Minneapolis, MN) at room temperature for 1 hour. After thorough washing, R-phycoerythrin (R-PE)–conjugated streptavidin (1:200; BD Biosciences, Oxford, UK) was added and incubated for another hour. Samples were mounted in mounting medium with DAPI (Vectashield; Vector Laboratories, Ltd., Peterborough, UK) and examined by confocal microscopy (LSM510 META; Carl Zeiss, Göttingen, Germany). 
Flat mounts of mouse eye were prepared for confocal microscopy by using a published method. 21 23 Briefly, retinal wholemounts were blocked and permeabilized with 5% (wt/vol) bovine serum albumin (BSA) and 0.3% (vol/vol) Triton-X100 in PBS at room temperature for 2 hours. The samples were then incubated overnight at 4°C with rabbit anti-mouse collagen IV (1:50; Serotec, Oxford, UK) and biotinylated Griffonia simplicifolia I isolectin B4 (1:100; Vector Laboratories, Ltd.) or anti Iba-1 (1:100; Abcam, Cambridge, UK) followed by fluorescein isothiocyanate (FITC)–conjugated anti-rabbit IgG (1:100; Zymed Laboratories, San Francisco, CA) and R-PE-conjugated streptavidin (BD Biosciences) at room temperature in the dark for 2 hours. The samples were observed by confocal microscopy. 
Results
Gene Expression Profile
The microarray data have been deposited in NCBI's Gene Expression Omnibus (GEO, http://ncbi.nim.nih.gov/geo/; provided in the public domain by the national Center for Biotechnology Information, Bethesda, MD) and are accessible through GEO Series accession number GSE17423. The mouse genome oligo microarray contains 43,379 gene features. Among them, 38,913 (89.7%) features were detected in retinal samples of 3-month-old mice (young), and 41,056 (92.6%) features were detected in samples of 20-month-old mice (old). Initially we used an adjusted P < 0.05 as well as a change ratio >2 to restrict the false discovery rate (FDR). The Limma Bioconductor program (moderated t-statistic) found 124 features to be differentially expressed in the young and old retinal samples. Ninety-seven features had increased expression, and 26 features had decreased expression in the aging retina. Among the 124 features, there were 67 classified genes. Since we had observed profound histologic and immunohistochemical changes in the aging mouse retina, 7 the fact that only 67 genes were differently expressed led us to believe that the adjusted P-value caused a significant level of false-negative results. Considering the fact that, when using the empiric Bayes shrinkage moderated t-test for a particular gene the error calculated is a combination of a gene-specific error estimate plus a global error estimate across all the genes, 13 it is clear that this moderated t-test has already considerably reduced the FDR in comparison to a standard t-test. 13 We decided therefore, to use a nonadjusted P-value for gene selection. However, to ensure a higher true discovery rate, we continued to apply the dual criteria of (1) a more than twofold change in gene expression and (2) a nonadjusted P < 0.01. With these criteria, 402 (0.93%) features (298 genes) displayed a more than twofold increase and 150 (0.24%) features (137 genes) displayed a more than twofold decrease in the retina of aged mice compared with that of young mice. These differentially expressed genes were then used as a guide for further pathway analysis. 
The DAVID-2008 system annotated genes with significant changes in the aging retina to different clusters on the basis of their biological functions. Figure 1A lists the selected clusters that may be relevant to retinal function. Genes involved in the following three functions were the most affected genes in the aged retina: stress stimuli response (94 genes), glycoprotein synthesis (90 genes), and regulation of biological processes (98 genes). Genes that were upregulated outnumbered those that were downregulated (Fig. 1A). Of the total 435 genes (298 upregulated and 137 downregulated) affected in the aging retina, 65 were immune response genes, and 39 were genes involved in defense responses (Fig. 1A). When the genes involved in the immune system were further clustered based on the immunologic processes, pathways involved in the innate immune system, including chemotaxis, endocytosis, complement activation, phagocytosis, and myeloid cell differentiation were predominantly affected (Fig. 1B). Genes involved in the immune system are listed in Table 2
Figure 1.
 
Changes in gene expression in the retina of old mice compared with that of young mice. (A) The number of genes involved in different biological pathways. (B) The number of genes involved in different immune pathways. All data are expressed as the number of genes either upregulated or downregulated.
Figure 1.
 
Changes in gene expression in the retina of old mice compared with that of young mice. (A) The number of genes involved in different biological pathways. (B) The number of genes involved in different immune pathways. All data are expressed as the number of genes either upregulated or downregulated.
Table 2.
 
List of Altered Genes Involved in the Immune Response in the Aging Retina
Table 2.
 
List of Altered Genes Involved in the Immune Response in the Aging Retina
Gene Symbol Gene Name Change Ratio P
Agtr1a Angiotensin II receptor, type 1a 14.35 4.21E-06
Alas2 Aminolevulinic acid synthase 2, erythroid 2.18 1.90E-03
B2m β-2 Microglobulin 2.02 3.30E-03
Bcl3 B-cell leukemia/lymphoma 3 2.44 6.73E-03
C1qα Complement component 1, q subcomponent, α polypeptide 2.21 2.97E-03
C1qc Complement component 1, q subcomponent, C chain 2.17 4.17E-03
C3 Complement component, 3 2.21 1.76E-03
C4b Complement component 4B (Childo blood group) 3.91 1.18E-03
Calcr Calcitonin receptor 46.75 5.00E-03
Ccl12 Chemokine (C-C motif) ligand 12 6.96 3.41E-04
Ccl2 Chemokine (C-C motif) ligand 2 4.80 3.24E-03
Ccl3 Chemokine (C-C motif) ligand 3 3.54 6.78E-03
Ccl8 Chemokine (C-C motif) ligand 8 3.13 6.62E-03
Cd200r CD200 receptor 1 2.19 6.53E-04
Cd36 CD36 antigen 2.49 2.38E-04
Cd48 CD48 antigen 2.94 1.65E-03
Cd8a CD8 antigen, α chain 2.36 1.74E-03
Cfb Complement factor B 4.70 6.45E-04
Clec4a2 C-type lectin domain family 4, member A2 3.10 6.40E-03
Clec4d C-type lectin domain family 4, member D 4.21 3.48E-04
Clec7a C-type lectin domain family 7, member A 2.44 1.78E-03
Csf3r Colony stimulating factor 3 receptor (granulocyte) 2.00 8.21E-03
Cxcl1 Chemokine (C-X-C motif) ligand 1 2.84 4.24E-03
Ddx58 Dead (ASP-GLU-ALA-ASP) box polypeptide 58 3.05 3.94E-03
Egr1 Early growth response 1 −3.27 5.65E-05
Eraf Erythroid associated factor 2.54 8.67E-05
Fcgr2b Fc receptor, IGG, low affinity IIB 2.05 4.15E-03
Fcgr3 Fc receptor, IGG, low affinity III 2.05 4.74E-03
Fyb FYN binding protein 2.01 8.13E-03
Gbp2 Guanylate nucleotide binding protein 2 2.19 9.50E-04
H2-bl Histocompatibility 2, blastocyst 2.03 3.91E-03
H2-kl Histocompatibility 2, K1, K region 2.13 7.05E-04
H2-Oa Histocompatibility 2, O region-α locus 2.17 4.61E-03
H2-Q1 Histocompatibility 2, Q region locus 1 2.56 3.93E-04
H2-T3 Histocompatibility 2, T region locus 3 −7.97 1.91E-03
Icam1 Intercellular adhesion molecule 1 4.42 1.20E-06
Ifi204 Interferon activated gene 204 2.90 5.61E-03
Ifit1 Interferon-induced protein with tetratricopeptide repeats 1 3.29 3.67E-03
Il13ra2 Interleukin 13 receptor, alpha 2 13.84 7.36E-06
Il28Rα Interleukin 28 receptor α 2.49 2.82E-04
Irf4 Interferon regulatory factory 4 2.03 3.98E-04
Itgam Integrin α M 2.03 6.00E-03
Itgax Integrin α X 5.37 1.07E-04
Kdr Kinase insert domain protein receptor −35.59 6.53E-03
Lilrb3 Leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), member 3 3.23 1.99E-03
Ly75 Lymphocyte antigen 75 3.26 1.43E-05
Masp1 Mannan-binding lectin serine peptidase 1 −3.02 6.38E-03
Masp2 Mannan-binding lectin serine peptidase 2 −2.17 4.57E-03
Mpa2l Macrophage activation 2 like 2.41 5.15E-03
Hbb-b1 Hemoglobin β-chain complex 2.76 3.18E-03
LOC56628 MHC (A.CA/J(H-2K-F) class I antigen 2.33 1.41E-04
Nod2 Caspase recruitment domain family, member 15 4.52 1.73E-06
Oas1a 2′-5′ Oligoadenylate synthetase 1A 3.59 2.72E-03
Oas1f 2′-5′ Oligoadenylate synthetase 1F 2.05 7.09E-03
Oas2 2′-5′ Oligoadenylate synthetase 2 3.98 1.76E-03
Oas3 2′-5′ Oligoadenylate synthetase 3 −4.62 2.69E-03
Oasl2 2′-5′ Oligoadenylate synthetase-like 2 2.03 7.66E-03
Plg Plasminogen −3.49 9.40E-04
Psmb8 Proteosome (Prosome, macropain) subunit, β type 8 (large multifunctional peptidase 7) 2.25 6.63E-04
S100a9 S100 calcium binding protein A9 (calgranulin B) 2.77 2.25E-03
Tlr2 Toll-like receptor 2 2.42 5.73E-03
Tlr4 Toll-like receptor 4 2.03 4.49E-03
Tlr6 Toll-like receptor 6 6.14 3.06E-03
Tnf Tumor necrosis factor 2.66 6.94E-04
Tshr Thyroid-stimulating hormone receptor 2.53 7.90E-03
Quantitative Real-Time RT-PCR (qPCR) Confirmation
To validate the microarray data, we performed quantitative PCR for 11 genes that showed significant changes in their expression in the retina of aged mice. The procedure was performed on separate mice in a second experiment. Six samples in each group were individually tested, and the entire experiment performed twice (see the Methods section). Therefore, the essential requirement for confirmation of the microarray analysis data by qPCR has been performed and the data validated for the genes are shown in Figure 2. Of the 11 genes tested, 10 showed changes similar to those detected in the microarray study (Fig. 2). Particularly notable are the increases in Cfb, Ccl2, and Calcr. Modest increases in Icam-1, C3, and Cd200r, were also observed as well as a notable decrease in Cdc25c. The change in Egr1 gene expression was not confirmed by qPCR (Fig. 2). Changes in other genes at this stage are under investigation. 
Figure 2.
 
Real-time quantitative RT-PCR confirmation of microarray data. (A) Relative mRNA expression levels of selected genes in the retinas of young and old mice (n = 6), results expressed as the mean ± SEM. (B) Comparison of the change ratios of the same selected genes determined by microarray analysis and real-time RT-PCR.
Figure 2.
 
Real-time quantitative RT-PCR confirmation of microarray data. (A) Relative mRNA expression levels of selected genes in the retinas of young and old mice (n = 6), results expressed as the mean ± SEM. (B) Comparison of the change ratios of the same selected genes determined by microarray analysis and real-time RT-PCR.
GO Analysis of Biological Functional Changes
To understand the biological relevance of the age-related gene changes in the retina, we used GO terms 24 to obtain information about the predominant biological processes or molecular functions of the genes that are differently expressed in the aging retina. The enrichment score, which indicates how the gene group involved in the important annotation terms associated with the total genes, was used as a criterion to select functional annotations. A higher enrichment score for a group indicates that gene members in that particular group play important roles in a given biological function. An enrichment score of 1.3 is equivalent to a non–log scale of 0.05 of the P-value and should be regarded as an indicator for further study. 15 A total of 133 clusters were generated from the selected gene list. Twenty-six of the 133 clusters had an enrichment score >1.3. The top cluster (annotation cluster 1) had an enrichment score of 14.06 (Table 3). Four biological functional pathways were grouped in cluster 1 (Table 3), and segregated into two categories: the immune response (Ir) and the response to injury (tissue stress/injury response, TS/I) (Table 3). The enrichment score in cluster 2 is 5.57; this cluster involves cell surface and outer plasma membrane functions (Table 3). Annotation cluster 3 also had a higher enrichment score (5.48), and the majority of the biological functions grouped in this cluster were immune-related with extremely low P-values (Table 3). This result suggests that Ir genes are the most frequently affected genes in the normal aging retina. The second most affected genes are those involved in tissue response to an injury stimulus and /or stress (TS/I genes) (Table 3). 
Table 3.
 
Gene Cluster with the Highest Enrichment Score
Table 3.
 
Gene Cluster with the Highest Enrichment Score
Functional Pathways Count (n) P* Benjamini†
Annotation Cluster 1, Enrichment Score: 14.06
Immune system process 65 3.2E-23 1.7E-19
Immune response 50 7.2E-21 1.9E-17
Defense response 39 3.0E-9 2.6E-6
Response to stimulus 94 8.5E-6 1.6E-3
Annotation Cluster 2, Enrichment Score: 5.57
Cell surface 18 7.3E-7 5.7E-4
External side of plasma membrane 13 9.9E-6 3.9E-3
Annotation Cluster 3, Enrichment Score: 5.48
Immune response 22 1.1E-13 9.6E-11
Inflammatory response 24 2.0E-11 3.4E-8
Innate immunity 13 3.2E-11 1.4E-8
Response to wounding 26 6.2E-10 8.1E-7
Response to external stimulus 31 6.0E-9 4.4E-6
Immune effector process 16 6.9E-8 4.0E-5
Adaptive immune response 13 1.8E-7 9.4E-5
Acute immune response 11 2.0E-6 7.3E-4
Regulation of immune response 13 2.4E-6 7.2E-4
Response to stress 36 2.8E-6 7.4E-4
Regulation of multicellular organismal process 18 4.5E-5 6.1E-3
Complement activation 7 1.3E-4 1.4E-2
We next analyzed the changes in gene expression in the context of known biological pathways. The GenMAPP program 16 showed that, among the various inflammatory pathways, complement activation pathways are the most affected in the aging retina (Fig. 3). A few genes involved in the classic complement pathway (C1qa, C1qg, C3, and C4) and the alternative complement pathway (CFB and C3) were upregulated (Fig. 3), whereas two genes involved in the lectin complement pathway (Masp 1 and Masp 2) were downregulated (Fig. 3). This result suggests that aging induces both the classic pathway and the alternative pathway of complement activation in the retina. 
Figure 3.
 
Pathway diagram showing the molecules involved in complement activation pathways and their interactions. The diagram was generated with GenMAPP software. 16
Figure 3.
 
Pathway diagram showing the molecules involved in complement activation pathways and their interactions. The diagram was generated with GenMAPP software. 16
Chemokines, in particular the members of the CC chemokine family, are another major group of inflammatory genes that were affected by age (Fig. 4). CCL2 and CCL12, which encode proteins that bind to CCR2, were upregulated (Fig. 4). Other chemokines, such as CCL3 and CCL8, were also upregulated in the aging retina (Fig. 4). One gene of the CXC chemokine family, CXCL1 was upregulated in the aging retina (Fig. 4). 
Figure 4.
 
Pathway diagram showing the molecules involved in the chemokine/chemokine receptor system. The diagram was generated with GenMAPP software. 16
Figure 4.
 
Pathway diagram showing the molecules involved in the chemokine/chemokine receptor system. The diagram was generated with GenMAPP software. 16
Complement Activation in the Retinas of Aged Mice
To further confirm the age-related upregulation of complement pathways in the neuroretina, we stained cryosections of retinal tissue with antibody against C3d and observed them by confocal microscopy. We have shown that the deposition of C3d at the RPE/Bruch's membrane increases with age, 7 suggesting that increased complement activation exists at the RPE/choroidal interface. In this study we further confirmed this observation (Figs. 5A, 5B, arrowhead). Furthermore, discrete C3d staining was observed throughout the ganglion cell layer to the inner plexiform layer of the neuroretina (Figs. 5B, 5C), suggesting that increased complement activation also exists in the neuroretina of aged mice. 
Figure 5.
 
Complement C3d deposition in the neuroretina. Cryosections of retina from 3-month-old mice (A) and 24-month-old mice (B, C) were stained for C3d (red) and DAPI (blue) and observed by confocal microscopy. Discrete C3d staining is seen in RPE/Bruch's membrane in the sample of a young mouse (A, arrowheads). Intensive C3d staining is seen through the ganglion layer to inner nuclear layer (B) of a 24-month old mouse. (C) High magnification showing C3d expression in the inner neuroretina layer. (D), isotype control staining. GL, ganglion layer; INL, inner nuclear layer; ONL, outer nuclear layer; RPE, retinal pigment epithelia.
Figure 5.
 
Complement C3d deposition in the neuroretina. Cryosections of retina from 3-month-old mice (A) and 24-month-old mice (B, C) were stained for C3d (red) and DAPI (blue) and observed by confocal microscopy. Discrete C3d staining is seen in RPE/Bruch's membrane in the sample of a young mouse (A, arrowheads). Intensive C3d staining is seen through the ganglion layer to inner nuclear layer (B) of a 24-month old mouse. (C) High magnification showing C3d expression in the inner neuroretina layer. (D), isotype control staining. GL, ganglion layer; INL, inner nuclear layer; ONL, outer nuclear layer; RPE, retinal pigment epithelia.
Microglial Activation in the Retinas of Aged Mice
In the retina, microglia are the main resident immune cells. Naïve nonactivated microglia produce basal levels of cytokines/chemokines for tissue homeostasis. During inflammation, microglia are activated and produce significant amounts of inflammatory cytokines/chemokines. Increased chemokine/chemokine receptor expression in the aging retina (Fig. 4) suggests that microglia are activated. To test our hypothesis, we stained flat mounts of retinas from young (3-month-old) and old (20–22-month-old) mice with biotinylated Griffonia simplicifolia I isolectin B4, a marker for activated microglial cells 25 and collagen IV antibody and examined them by confocal microscopy. All retinal vessels were stained with collagen IV (Fig. 6). In the retinas of young mice, discrete regions of vascular endothelial cells, in particular the sites of branching vessels, were positive for isolectin B4 (Figs. 6A, 6B, arrows); however, no isolectin B4–positive microglia were observed (Figs. 6A, 6B). In contrast, in the retinas of old mice, many isolectin B4+ cells were observed in retinal superficial layer (Fig. 6C) as well as in the bipolar layer and surrounding area (Fig. 6D). Isolectin B4+ cells presented as small round/ovoid, or amoebic shapes with short dendrites (Figs. 6C, 6D) in the inner retina. Double staining of retinal microglia with isolectin B4 and Iba-1, a microglial marker, 22,26 showed that all isolectin B4-positive cells were Iba-1+ microglia (Figs. 6E–G, arrowheads), but not all Iba-1+ cells were positive for isolectin B4 (Figs. 6E–G), indicating that isolectin B4–positive cells are a subset of Iba-1+ microglial cells. 
Figure 6.
 
Microglial activation in the aging retina. Retinal flat mounts from 3-month-old (A, B) and 24-month-old (CG) mice were stained for isolectin B4 (red, AG) and collagen IV (green, AD) or microglial marker Iba-1 (green, F, G) and observed by confocal microscopy. Images shown are reconstructed z-stack images taken from the superficial (A, C, EG) and inner plexiform (B, D) layers. Isolectin B4 staining for activated microglial cells as well as endothelial cells (AD). In the retina of a young mouse, only endothelial cells were positive for isolectin B4 (A, arrows). Two Iba-1+ microglia were stained positive for isolectin B4 (EG, arrowheads).
Figure 6.
 
Microglial activation in the aging retina. Retinal flat mounts from 3-month-old (A, B) and 24-month-old (CG) mice were stained for isolectin B4 (red, AG) and collagen IV (green, AD) or microglial marker Iba-1 (green, F, G) and observed by confocal microscopy. Images shown are reconstructed z-stack images taken from the superficial (A, C, EG) and inner plexiform (B, D) layers. Isolectin B4 staining for activated microglial cells as well as endothelial cells (AD). In the retina of a young mouse, only endothelial cells were positive for isolectin B4 (A, arrows). Two Iba-1+ microglia were stained positive for isolectin B4 (EG, arrowheads).
Discussion
The data in this study of the aging retina in the mouse, determined by microarray analysis of gene expression in samples of RNA from retinal extracts and validated by qPCR analysis of cDNA retinal samples as well as selected immunohistochemical analysis of retinal tissues, reveal two functional genetic pathways to be mostly affected by aging: the Ir genes and the TS/I genes. The largest change was observed in the Calcr gene, which encodes the calcitonin receptor. The expression of the gene that encodes calcitonin/calcitonin-related polypeptide (Calc, Agilent probe number, A_51_P290826) was also upregulated in the aging retina in our microarray assay (change: 2.99-fold, P = 0.00047). 
Calcitonin, a 32-amino-acid peptide hormone is produced mainly by the parafollicular cells of the thyroid gland in response to increases in extracellular calcium concentrations, binds to Calcr and regulates Ca2+ concentrations via bone resorption in osteoclasts. The role of Calcr in retinal homeostasis and retinal aging remains elusive. It is a member of the subfamily of class II 7-transmembrane G-protein–coupled receptors and is widely expressed in tissues including the central nervous system (CNS) and the kidney, and occurs in two isoforms, Cr1a, and Cr1b, both of which occur in the CNS. 8 Recently, a Lac Z reporter system used in transgenic mice showed Calcr in the developing CNS and the brain and also in adult retina. 27 However, upregulation of the Calcr in the retina has not been shown, and the significance of its very marked upregulation in the aging retina reported in the current work is unclear. The precursor of calcitonin (procalcitonin) is an acute phase protein which is widely known to be elevated in the blood in sepsis and bacterial infection and indeed is used as a biomarker for infection. 12,28 Procalcitonin has been shown to augment proinflammatory cytokines including IL-β, TNF-α, and IL-8 synthesis in blood monocytes 11 and nitric oxide (NO) production in vascular smooth muscle cells. 29 On the other hand, calcitonin has been reported to diminish local inflammation after various forms of injury in rats. 30 The upregulation of Calcr may therefore reflect a low-grade chronic proinflammatory state associated with aging, and thus it may represent a tissue biomarker of aging, as well as have a physiological role in regulation of para-inflammation. 
Apart from the Calcr gene, several other genes involved in inflammation were differentially expressed in the aging retina. The Ir and TS/I genes pathways are closely related physiologically. On the one hand, in response to insult/stress, tissues and cells may activate an anti-insult/stress system by producing stress-related molecules including heat-shock proteins, anti-oxidant enzymes, and many other regulatory molecules; on the other hand, tissue immune cells are activated by endogenous alarmins 31 released from damaged tissue/cells and participate in a tissue repair/wound healing processes. According to the free radical theory of aging of Harman, 5 aging is accompanied with increased oxidative stress. It is well recognized that with age, increased oxidative stress exists in the retina, in particular at the macular area, and the data in the present study in neural retina complements that of a recent work in aged RPE/choroidal tissues. 32 Using microarray analysis, Chen et al. 32 observed increased complement activation and leukocyte recruitment in aged RPE/choroidal tissue, indicating that there may be an age-dependent chronic inflammatory process in the RPE/choroid. However, they did not find significant inflammatory changes in the aging neural retina. 32 The discrepancy between our observation and that of Chen et al. 32 may be attributable to differences in gene chips, microarray techniques, or criteria used for data analysis, but it might be expected that age-related changes in the RPE/choroid are reflected by similar changes in the aging neural retina. 
The immune system, in particular the innate immune system, plays an important role in tissue homeostasis. With age, increased oxidative stress may damage retinal tissue resulting in retinal cell apoptosis or the formation of a variety of oxidized molecules. 7 In the aging retina, two inflammatory pathways were affected as a result of age-related tissue stress: the complement cascade and tissue resident macrophage (retinal microglia) activation pathway, suggesting a low-grade chronic inflammation. This low-grade chronic inflammation induced by endogenous noxious stress has recently been proposed by Medzhitov 6 to represent a modified form of inflammation, which he termed para-inflammation, in the sense that such an adaptive response has characteristics that are intermediate between a basal noninflammatory state and inflammation proper. 6 Indeed, he suggested that the physiological significance of para-inflammation is to maintain tissue homeostasis and to restore tissue functionality. However, like any physiological process, this is modified in individuals by genetic and environmental factors and may in certain circumstances be exaggerated, leading to pathologic effects. We suggest that this may be how complement activation in age-related macular degeneration 33,34 can be viewed. 
Tissue-resident macrophages (e.g., microglial cells in the retina) are considered the cells in the immune system that are responsible for the para-inflammatory response by clearing apoptotic cells as well as damaged or modified self-molecules to maintain tissue homeostasis. 6 We showed that retinal microglia migrate from the inner retina to outer retina with age and accumulate in the subretinal space. 22 Subretinal microglia have a large cell body with short dendrites, contain autofluorescent lipofuscin granules, and have an activated phenotype. 22 These observations suggest an increased tissue insult in the subretinal space in the aging eye. In this study of flat mount retinal preparations, isolectin B4+ activated microglia were also observed in the inner neuroretina of aged mice (Figs. 6C, 6D). In addition, complement C3d deposition was observed in the inner retinal layer in aged mice (Fig. 5), indicating increased tissue stress in the inner layers of neuroretina of aged mice, as well as in the outer layers, as shown in prior studies. Activated microglia in this layer may have implications for neuroprotection of ganglion cells, an important consideration in glaucoma and other neural degenerative disorders. 
How complement and microglia are activated in the aging retina in normal physiological conditions is not known. With age, breakdown of the blood–retinal barrier has been observed in rats under normal physiological conditions. 35 It is possible that plasma-derived proteins including chemokines and the complement components may leak into retinal parenchyma through damaged vessels and activate the complement cascade. However, the evidence that there is increased expression of genes for chemokines/chemokine receptors and complement components in the aging retina points to the existence of a local immune response regulatory mechanism. We have shown that complement factor H (CFH) 19 and factor B (CFB) 20 are produced locally in the eye by RPE cells and that the production of CFH and CFB is regulated by oxidative stress and inflammatory cytokines, supporting the hypothesis that a local immune regulatory system may exist in the retina. 
Although it is well established that microglial and complement activation are both involved in several age-related neurodegenerative diseases including Alzheimer's disease 36,37 and Huntington's disease 36 in the brain, and glaucoma, 38 diabetic retinopathy, 39 and age-related macular degeneration 3,40 in the retina, we propose that these para-inflammatory responses may have a double-edged effect during the normal aging process. Under normal physiological conditions, a certain level of complement and microglial activation may be necessary to repair minor tissue damage caused by age-related endogenous stress and also possible for tissue remodeling. On the other hand, excessive uncontrolled complement or microglial activation may produce overt inflammatory responses with marked release of cytokines/chemokines causing irreparable damage to the neuroretina (and brain in Alzheimer's or Huntington's disease) (see for instance the increase in CCL2 and CCL12 in the present study). A full understanding of the age-related para-inflammatory response and its regulation in the neuroretina may provide critical information on the pathogenesis of a variety of age-related retinal neurodegenerative diseases including glaucoma, diabetic retinopathy and age-related macular degeneration. 
Footnotes
 Supported by NHSG Endowment (07/44), the R. S. MacDonald Trust, and the Development Trust of the University of Aberdeen.
Footnotes
 Disclosure: M. Chen, None; E. Muckersie, None; J.V. Forrester, None; H. Xu, None
The authors thank Claus-Dieter Mayer of the Rowett Institute, University of Aberdeen, for his advice on microarray data analysis. 
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Figure 1.
 
Changes in gene expression in the retina of old mice compared with that of young mice. (A) The number of genes involved in different biological pathways. (B) The number of genes involved in different immune pathways. All data are expressed as the number of genes either upregulated or downregulated.
Figure 1.
 
Changes in gene expression in the retina of old mice compared with that of young mice. (A) The number of genes involved in different biological pathways. (B) The number of genes involved in different immune pathways. All data are expressed as the number of genes either upregulated or downregulated.
Figure 2.
 
Real-time quantitative RT-PCR confirmation of microarray data. (A) Relative mRNA expression levels of selected genes in the retinas of young and old mice (n = 6), results expressed as the mean ± SEM. (B) Comparison of the change ratios of the same selected genes determined by microarray analysis and real-time RT-PCR.
Figure 2.
 
Real-time quantitative RT-PCR confirmation of microarray data. (A) Relative mRNA expression levels of selected genes in the retinas of young and old mice (n = 6), results expressed as the mean ± SEM. (B) Comparison of the change ratios of the same selected genes determined by microarray analysis and real-time RT-PCR.
Figure 3.
 
Pathway diagram showing the molecules involved in complement activation pathways and their interactions. The diagram was generated with GenMAPP software. 16
Figure 3.
 
Pathway diagram showing the molecules involved in complement activation pathways and their interactions. The diagram was generated with GenMAPP software. 16
Figure 4.
 
Pathway diagram showing the molecules involved in the chemokine/chemokine receptor system. The diagram was generated with GenMAPP software. 16
Figure 4.
 
Pathway diagram showing the molecules involved in the chemokine/chemokine receptor system. The diagram was generated with GenMAPP software. 16
Figure 5.
 
Complement C3d deposition in the neuroretina. Cryosections of retina from 3-month-old mice (A) and 24-month-old mice (B, C) were stained for C3d (red) and DAPI (blue) and observed by confocal microscopy. Discrete C3d staining is seen in RPE/Bruch's membrane in the sample of a young mouse (A, arrowheads). Intensive C3d staining is seen through the ganglion layer to inner nuclear layer (B) of a 24-month old mouse. (C) High magnification showing C3d expression in the inner neuroretina layer. (D), isotype control staining. GL, ganglion layer; INL, inner nuclear layer; ONL, outer nuclear layer; RPE, retinal pigment epithelia.
Figure 5.
 
Complement C3d deposition in the neuroretina. Cryosections of retina from 3-month-old mice (A) and 24-month-old mice (B, C) were stained for C3d (red) and DAPI (blue) and observed by confocal microscopy. Discrete C3d staining is seen in RPE/Bruch's membrane in the sample of a young mouse (A, arrowheads). Intensive C3d staining is seen through the ganglion layer to inner nuclear layer (B) of a 24-month old mouse. (C) High magnification showing C3d expression in the inner neuroretina layer. (D), isotype control staining. GL, ganglion layer; INL, inner nuclear layer; ONL, outer nuclear layer; RPE, retinal pigment epithelia.
Figure 6.
 
Microglial activation in the aging retina. Retinal flat mounts from 3-month-old (A, B) and 24-month-old (CG) mice were stained for isolectin B4 (red, AG) and collagen IV (green, AD) or microglial marker Iba-1 (green, F, G) and observed by confocal microscopy. Images shown are reconstructed z-stack images taken from the superficial (A, C, EG) and inner plexiform (B, D) layers. Isolectin B4 staining for activated microglial cells as well as endothelial cells (AD). In the retina of a young mouse, only endothelial cells were positive for isolectin B4 (A, arrows). Two Iba-1+ microglia were stained positive for isolectin B4 (EG, arrowheads).
Figure 6.
 
Microglial activation in the aging retina. Retinal flat mounts from 3-month-old (A, B) and 24-month-old (CG) mice were stained for isolectin B4 (red, AG) and collagen IV (green, AD) or microglial marker Iba-1 (green, F, G) and observed by confocal microscopy. Images shown are reconstructed z-stack images taken from the superficial (A, C, EG) and inner plexiform (B, D) layers. Isolectin B4 staining for activated microglial cells as well as endothelial cells (AD). In the retina of a young mouse, only endothelial cells were positive for isolectin B4 (A, arrows). Two Iba-1+ microglia were stained positive for isolectin B4 (EG, arrowheads).
Table 1.
 
Primer Sequences and Probe Numbers Used for qPCR Study
Table 1.
 
Primer Sequences and Probe Numbers Used for qPCR Study
Primer Name Primer Sequence (5′–3′) Probe No.*
Ms4a6b
    Forward TGC CGC CAA GTC TGT TCT 109
    Reverse CAG GCC AAG TTC CAA CAT AGT
Cdc25c
    Forward GGA AAC ACC CGG ATC TGA A 26
    Reverse ACT TTC CAG ACA GCA AAG CAG
Cyp2e1
    Forward GTCTCC TCA TAG AGA TGG AGA AGG 76
    Reverse AGT CAC AGA AAT ATT TTC CAT TGT GT
Agtr1a
    Forward ACTCACAGCAACCCTCCAAG 9
    Reverse CTCAGACACTGTTCAAAATGCAC
Erg1
    Forward CCCTATGAGCACCTGACCAC 22
    Reverse TCGTTTGGCTGGGATAACTC
Cfb
    Forward CTCGAACCTGCAGATCCAC 1
    Reverse TCAAAGTCCTGCGGTCGT
Cd200r
    Forward AAGAATCAAACAACACAGAACAACA 82
    Reverse CTGTACAGACACTGTAGTGTTCACTTG
Icam1
    Forward CCCACGCTACCTCTGCTC 81
    Reverse GATGGATACCTGAGCATCACC
C3
    Forward AGCAGGTCATCAAGTCAGGC
    Reverse GATGTAGCTGGTGTTGGGCT
Calcr
    Forward GCCTCCCCATTTACATCTGC
    Reverse CTCCTCGCCTTCGTTGTTG
Ccl2 Assay kit for ccl2 (Taqman; Applied Biosystems)
Table 2.
 
List of Altered Genes Involved in the Immune Response in the Aging Retina
Table 2.
 
List of Altered Genes Involved in the Immune Response in the Aging Retina
Gene Symbol Gene Name Change Ratio P
Agtr1a Angiotensin II receptor, type 1a 14.35 4.21E-06
Alas2 Aminolevulinic acid synthase 2, erythroid 2.18 1.90E-03
B2m β-2 Microglobulin 2.02 3.30E-03
Bcl3 B-cell leukemia/lymphoma 3 2.44 6.73E-03
C1qα Complement component 1, q subcomponent, α polypeptide 2.21 2.97E-03
C1qc Complement component 1, q subcomponent, C chain 2.17 4.17E-03
C3 Complement component, 3 2.21 1.76E-03
C4b Complement component 4B (Childo blood group) 3.91 1.18E-03
Calcr Calcitonin receptor 46.75 5.00E-03
Ccl12 Chemokine (C-C motif) ligand 12 6.96 3.41E-04
Ccl2 Chemokine (C-C motif) ligand 2 4.80 3.24E-03
Ccl3 Chemokine (C-C motif) ligand 3 3.54 6.78E-03
Ccl8 Chemokine (C-C motif) ligand 8 3.13 6.62E-03
Cd200r CD200 receptor 1 2.19 6.53E-04
Cd36 CD36 antigen 2.49 2.38E-04
Cd48 CD48 antigen 2.94 1.65E-03
Cd8a CD8 antigen, α chain 2.36 1.74E-03
Cfb Complement factor B 4.70 6.45E-04
Clec4a2 C-type lectin domain family 4, member A2 3.10 6.40E-03
Clec4d C-type lectin domain family 4, member D 4.21 3.48E-04
Clec7a C-type lectin domain family 7, member A 2.44 1.78E-03
Csf3r Colony stimulating factor 3 receptor (granulocyte) 2.00 8.21E-03
Cxcl1 Chemokine (C-X-C motif) ligand 1 2.84 4.24E-03
Ddx58 Dead (ASP-GLU-ALA-ASP) box polypeptide 58 3.05 3.94E-03
Egr1 Early growth response 1 −3.27 5.65E-05
Eraf Erythroid associated factor 2.54 8.67E-05
Fcgr2b Fc receptor, IGG, low affinity IIB 2.05 4.15E-03
Fcgr3 Fc receptor, IGG, low affinity III 2.05 4.74E-03
Fyb FYN binding protein 2.01 8.13E-03
Gbp2 Guanylate nucleotide binding protein 2 2.19 9.50E-04
H2-bl Histocompatibility 2, blastocyst 2.03 3.91E-03
H2-kl Histocompatibility 2, K1, K region 2.13 7.05E-04
H2-Oa Histocompatibility 2, O region-α locus 2.17 4.61E-03
H2-Q1 Histocompatibility 2, Q region locus 1 2.56 3.93E-04
H2-T3 Histocompatibility 2, T region locus 3 −7.97 1.91E-03
Icam1 Intercellular adhesion molecule 1 4.42 1.20E-06
Ifi204 Interferon activated gene 204 2.90 5.61E-03
Ifit1 Interferon-induced protein with tetratricopeptide repeats 1 3.29 3.67E-03
Il13ra2 Interleukin 13 receptor, alpha 2 13.84 7.36E-06
Il28Rα Interleukin 28 receptor α 2.49 2.82E-04
Irf4 Interferon regulatory factory 4 2.03 3.98E-04
Itgam Integrin α M 2.03 6.00E-03
Itgax Integrin α X 5.37 1.07E-04
Kdr Kinase insert domain protein receptor −35.59 6.53E-03
Lilrb3 Leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), member 3 3.23 1.99E-03
Ly75 Lymphocyte antigen 75 3.26 1.43E-05
Masp1 Mannan-binding lectin serine peptidase 1 −3.02 6.38E-03
Masp2 Mannan-binding lectin serine peptidase 2 −2.17 4.57E-03
Mpa2l Macrophage activation 2 like 2.41 5.15E-03
Hbb-b1 Hemoglobin β-chain complex 2.76 3.18E-03
LOC56628 MHC (A.CA/J(H-2K-F) class I antigen 2.33 1.41E-04
Nod2 Caspase recruitment domain family, member 15 4.52 1.73E-06
Oas1a 2′-5′ Oligoadenylate synthetase 1A 3.59 2.72E-03
Oas1f 2′-5′ Oligoadenylate synthetase 1F 2.05 7.09E-03
Oas2 2′-5′ Oligoadenylate synthetase 2 3.98 1.76E-03
Oas3 2′-5′ Oligoadenylate synthetase 3 −4.62 2.69E-03
Oasl2 2′-5′ Oligoadenylate synthetase-like 2 2.03 7.66E-03
Plg Plasminogen −3.49 9.40E-04
Psmb8 Proteosome (Prosome, macropain) subunit, β type 8 (large multifunctional peptidase 7) 2.25 6.63E-04
S100a9 S100 calcium binding protein A9 (calgranulin B) 2.77 2.25E-03
Tlr2 Toll-like receptor 2 2.42 5.73E-03
Tlr4 Toll-like receptor 4 2.03 4.49E-03
Tlr6 Toll-like receptor 6 6.14 3.06E-03
Tnf Tumor necrosis factor 2.66 6.94E-04
Tshr Thyroid-stimulating hormone receptor 2.53 7.90E-03
Table 3.
 
Gene Cluster with the Highest Enrichment Score
Table 3.
 
Gene Cluster with the Highest Enrichment Score
Functional Pathways Count (n) P* Benjamini†
Annotation Cluster 1, Enrichment Score: 14.06
Immune system process 65 3.2E-23 1.7E-19
Immune response 50 7.2E-21 1.9E-17
Defense response 39 3.0E-9 2.6E-6
Response to stimulus 94 8.5E-6 1.6E-3
Annotation Cluster 2, Enrichment Score: 5.57
Cell surface 18 7.3E-7 5.7E-4
External side of plasma membrane 13 9.9E-6 3.9E-3
Annotation Cluster 3, Enrichment Score: 5.48
Immune response 22 1.1E-13 9.6E-11
Inflammatory response 24 2.0E-11 3.4E-8
Innate immunity 13 3.2E-11 1.4E-8
Response to wounding 26 6.2E-10 8.1E-7
Response to external stimulus 31 6.0E-9 4.4E-6
Immune effector process 16 6.9E-8 4.0E-5
Adaptive immune response 13 1.8E-7 9.4E-5
Acute immune response 11 2.0E-6 7.3E-4
Regulation of immune response 13 2.4E-6 7.2E-4
Response to stress 36 2.8E-6 7.4E-4
Regulation of multicellular organismal process 18 4.5E-5 6.1E-3
Complement activation 7 1.3E-4 1.4E-2
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