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Published in final edited form as: J Psychiatr Res. 2020 Jun 6;129:61–65. doi: 10.1016/j.jpsychires.2020.06.005

Epigenetics of neuroinflammation: immune response, inflammatory response and cholinergic synaptic involvement evidenced by genome-wide DNA methylation analysis of delirious inpatients

Taku Saito a,b, Hiroyuki Toda b, Gabrielle N Duncan a, Sydney S Jellison a, Tong Yu a, Mason J Klisares a, Sophia Daniel a, Allison Andreasen a, Lydia Leyden a, Mandy Hellman a, Eri Shinozaki c, Sangil Lee d, Aihide Yoshino b, Hyunkeun R Cho e, Gen Shinozaki a,f,g,h,*
PMCID: PMC7486988  NIHMSID: NIHMS1623058  PMID: 32590150

Abstract

Previously our study has shown that the DNA methylation (DNAm) levels at CpG sites in the pro-inflammatory cytokine gene, TNF-alpha, decrease along with aging, suggesting the potential role of DNAm in aging and heightened inflammatory process leading to increased risk for delirium. However, DNAm differences between delirium cases and non-delirium controls have not been investigated directly. Therefore, we examined genome-wide DNAm differences in blood between patients with delirium and controls to identify useful epigenetic biomarkers for delirium. Data from a total of 87 subjects (43 delirium cases) were analyzed by a genome-wide DNAm case-control association study. A genome-wide significant CpG site near the gene of LDLRAD4 was identified (p = 5.07E-8). In addition, over-representation analysis showed several significant pathways with a false discovery rate adjusted p-value < 0.05. The top pathway with a Gene Ontology term was immune response, and the second top pathway with a Kyoto Encyclopedia of Genes and Genomes term was cholinergic synapse. Significant DNAm differences related to immune/inflammatory response were shown both at gene and pathway levels between patients with delirium and non-delirium controls. This finding indicates that DNAm status in blood has the potential to be used as epigenetic biomarkers for delirium.

Keywords: Delirium, Aging, Genome-wide DNA methylation, immune response, inflammatory response

1. Introduction

Delirium among elderly patients is dangerous and common—it occurs in 15–53% of elderly patients after surgery, and in 70–87% of those in intensive care (Inouye, 2006); however, it is underdiagnosed and undertreated (Spronk et al., 2009). Delirium is notorious for its association with a long term cognitive decline (Pandharipande et al., 2013) and high mortality (McCusker et al., 2002; Witlox et al., 2010). Given that our society is aging, it is becoming increasingly important to predict which patients are at risk of experiencing delirium. Previous searches for biomarkers have revealed increased serum levels of inflammatory markers (Dillon et al., 2017; Vasunilashorn et al., 2017) and cytokines (Khan et al., 2011; Vasunilashorn et al., 2015) among elderly patients with delirium. Similarly, studies in animal models have shown that cognitive disturbances (Hovens et al., 2015) in response to exogenous insults increase with age and that cytokine release from microglia play a key role (Xu et al., 2014). Thus, it is possible that microglia and inflammatory cytokines play a role in the pathogenesis of delirium in humans. However, the mechanisms whereby cytokine release or neuroinflammation is enhanced with aging remain unclear, and identifying the patients in whom this occurs and optimizing their care will require reliable biomarkers.

Given the fact that aging and inflammation are the key risk factors of delirium (Shinozaki et al., 2018a), we focused on the fact that DNA methylation (DNAm) changes dynamically over the human lifespan and that epigenetic mechanisms control the expression of genes including those of cytokines. Thus, we hypothesized that epigenetic modifications specific to aging and delirium susceptibility occur in microglia; that similar modifications occur in blood; and that these epigenetic changes enhance reactions to exogenous insult, resulting in increased cytokine expression and delirium susceptibility (Shinozaki et al., 2018a). In fact, no published study has assessed DNAm and its relationship to delirium in humans, especially with genome-wide DNAm investigation.

To support this hypothesis of DNAm change along with aging among pro-inflammatory cytokines, we previously reported that DNAm levels in blood decrease along with aging on the pro-inflammatory cytokine gene TNF-alpha, based on 265 participants from the Grady Trauma Project (GTP) (Shinozaki et al., 2018a). We also showed that expression level of TNF-alpha among the same subjects increased with aging (Shinozaki et al., 2018a). This data supports our hypothesis that in pro-inflammatory cytokine genes, DNAm levels decrease along with aging, and expression level increase. In addition, using a unique dataset from our own study comparing DNAm status from neurosurgically resected live human brains followed by fluorescence-activated cell sorting (FACS), we showed that DNAm of TNF-alpha universally decreases with age among the glial (neuronal negative) component, but no such patterns were found among neurons (neuronal positive component) (Shinozaki et al., 2018a). This data also supports our hypothesis that the DNAm in pro-inflammatory genes decrease in glia along with aging, making microglia potentially more prone to express those cytokine genes and to have heightened inflammatory response when exposed to external stimuli such as surgery or infection leading to delirium.

However, what was lacking in our previous data was that we did not directly test DNAm differences between delirium cases and non-delirium controls. To fill this gap, we conducted the present study to compare DNAm status in blood from hospitalized patients with and without delirium to identify clinically useful epigenetic biomarkers for delirium from blood samples, which are routinely obtained from patients. We used blood for three reasons: 1) the function of monocytes in the blood is similar to that of microglia in that both release cytokines in response to exogenous stimulus, 2) our comparison of DNAm levels in live brain tissue (resected during neurosurgery) to those in blood from same individual collected at the same time point showed a high level of correlation (r = 0.86) (Braun et al., 2019), and 3) our previous data showed a similar age-associated decrease in DNAm in the pro-inflammatory cytokine gene TNF-alpha among glia and blood (Shinozaki et al., 2018a). To test our hypothesis that DNAm changes related to immune/inflammatory response was associated with delirium, and to identify useful epigenetic biomarkers for delirium, we conducted the present study investigating DNAm differences in blood between delirium patients and non-delirium controls.

To be comprehensive, we employed a genome-wide approach using Illumina EPIC array. We conducted over-representation analysis by using the top differentially methylated CpGs between the two groups for both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms.

2. Material and Methods

2.1. Study Design, Subjects and Sample Collection

A genome-wide DNAm case-control association study was conducted. A more detailed overview of study participants’ recruitment process has been described previously (Shinozaki et al., 2018b). Briefly, subjects who admitted to the University of Iowa Hospitals and Clinics were recruited for this epigenetics study between November 2017 and October 2018. Initially, ninety-two age- and sex-matched subjects were selected for this epigenetics analysis. The definition of delirium status is described in the following paragraph. Controls were defined as those who did not meet the criteria for delirium.

2.2. Delirium Status Definition

A detailed description of study participants’ phenotyping can be found in the previous report (Shinozaki et al., 2018b). Briefly, we screened potential study participants for the presence of delirium by reviewing hospital records and by administering the Confusion Assessment Method for Intensive Care Unit (CAM-ICU) (Ely et al., 2001), the DRS-R-98 (Trzepacz et al., 2001), and the DOSS (Schuurmans et al., 2003). Subjects were categorized to be potential delirium cases if they showed CAM-ICU positive, DRS-R-98 score ≥19, or DOSS score ≥3. The final decision of delirium category for each subject was made by a trained psychiatrist (G.S.) based on detailed chart review.

2.3. Sample Processing and Epigenetics Methods

Written informed consent was obtained, and whole blood samples were collected in EDTA tubes. All samples were stored at −80 °C. Methylome assays were performed as previously described (Braun et al., 2019). Briefly, genomic DNA was isolated from whole blood with the MasterPure™ DNA Purification kit (Epicentre, MCD85201). Genomic DNA was bisulfite-converted with the EZ DNA Methylation™ Kit (Zymo Research, D5002). DNAm of 93 samples (two samples were from one subject) was analyzed with the Infinium HumanMethylationEPIC BeadChip™ Kit (Illumina, WG-317–1002). Raw data was processed with the R packages ChAMP (Morris et al., 2014) and minfi (Aryee et al., 2014; Fortin et al., 2017). One sample was filtered out because it had > 0.1 of CpG sites with detection p-values > 0.01, and CpG sites were filtered out as described below with ChAMP. As a result, 92 samples and 701,196 probes remained. Then, quality control and exploratory analysis were conducted. The density and multidimensional scaling plots showed 5 outliers. Two of them were the duplicate samples from one subject. The remaining 87 samples were processed again using ChAMP. The probes were filtered out if they 1) had a detection p-value > 0.01 (12,903 probes), 2) had less than 3 beads in at least 5% of samples per probe (8,280 probes), 3) had non-CpG probes (2,911 probes), 4) had SNP-related probes (Zhou et al., 2017) (94,425 probes), 5) had multi-hit probes (Nordlund et al., 2013) (11 probes), and 6) were located in chromosomes X or Y (16,356 probes). Eighty-seven samples and 731,032 probes remained by filtering. Beta mixture quantile dilation (Teschendorff et al., 2013) was used to normalize samples. The combat normalization method was used to correct for batch effect (Johnson et al., 2007; Leek et al., 2019).

2.4. Statistical Analysis

All statistical analyses were performed using R (R Core Team, 2019). The categorical data was calculated with chi-square test. Cell type proportions of CD8 T cells, CD4 T cells, natural killer cells, B cells, monocytes, and granulocytes were estimated using the online DNAm Age Calculator (Horvath, 2013, 2019) by using the method described in the previous study (Houseman et al., 2012). Differential DNA methylation at the level of individual CpGs was analyzed by RnBeads using the limma method (Assenov et al., 2014; Ritchie et al., 2015). Age, gender, and cell type proportions were included as covariates. Over-representation analysis for GO and KEGG terms was conducted using the gometh analysis in the R package missMethyl (Phipson et al., 2016) by correcting for the number of CpG probes in each gene. A p-value of less than 0.05/731,032 = 6.84E-08 was determined to be genome-wide significant.

3. Results

3.1. Study Subjects Demographics

Among 92 subjects enrolled for this study, data from 43 delirium case and 44 non-delirium control subjects were included for the analysis after filtering process (age average 70.2 years, SD 10.2, range 42–101 years). Age and gender proportions were not statistically different between cases and controls. The total scores of the Delirium Rating Scale - Revised-98 (DRS-R-98) and the Delirium Observation Screening Scale (DOSS) were significantly higher in delirium cases than in non-delirium controls (Supplementary Table 1). There were no significant differences in CD8 T cells, natural killer cells, monocytes, and granulocytes between delirium cases and non-delirium controls (Supplementary Table 1).

3.2. Comparison of Genome-wide DNAm Differences

We directly tested DNAm differences between delirium cases and non-delirium controls. Volcano plots for the distribution of individual CpG differences and their corresponding logarithmic transformed p-values are shown in Figure 1. The top 20 differentially methylated CpGs between delirium cases and non-delirium controls are shown in Supplementary Table 2. Genome-wide analysis showed one genome-wide significanct (p = 5.07E-8) CpG site at cg21295729. This CpG is located near the gene LDLRAD4. The second top hit CpG was cg10518911 which is located near the gene DAPK1 (p = 1.13E-7).

Figure 1.

Figure 1

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Volcano plots: the distribution of individual CpGs and their logarithmic transformed p-values (n = 87)

3.3. Over-representation Analysis

Over-representation analysis was conducted by using the 753 CpGs with methylation level differences greater than 5.0% and a p-value <0.0005. By using genes near those CpG sites, over-representation analysis showed the following results of the top 20 pathways with GO (Table 1) and KEGG analysis (Table 2). The top pathways from GO analysis were immune response and myeloid leukocyte activation (Table 1). From KEGG analysis were aldosterone synthesis/secretion and cholinergic synapse (Table 2). Supplementary Table 3 also shows the significant pathways of GO analysis with false discovery rate (FDR)-adjusted p-value < 0.05. The pathways of inflammatory response was also significant (FDR-adjusted p-value <0.05) from GO terms (Supplementary Table 3).

Table 1:

Result of the top 20 pathways of GO analysis with differentially methylated CpGs between delirium cases and non-delirium controls

Term Ont N DE p-value FDR
immune response BP 1925 82 3.99E-10 7.11E-06
myeloid leukocyte activation BP 635 41 6.24E-10 7.11E-06
cell activation involved in immune response BP 698 42 1.60E-09 1.21E-05
leukocyte activation involved in immune response BP 694 41 3.60E-09 2.05E-05
cell activation BP 1331 65 8.84E-09 4.03E-05
neutrophil activation BP 496 33 1.13E-08 4.28E-05
granulocyte activation BP 503 33 1.40E-08 4.56E-05
leukocyte activation BP 1181 58 2.17E-08 6.18E-05
neutrophil degranulation BP 483 32 2.57E-08 6.31E-05
neutrophil activation involved in immune response BP 486 32 2.77E-08 6.31E-05
myeloid cell activation involved in immune response BP 538 34 3.11E-08 6.44E-05
cytoplasmic vesicle CC 2261 96 4.05E-08 7.13E-05
intracellular vesicle CC 2264 96 4.21E-08 7.13E-05
neutrophil mediated immunity BP 497 32 4.39E-08 7.13E-05
leukocyte degranulation BP 529 32 2.13E-07 2.93E-04
immune system process BP 2807 103 2.17E-07 2.93E-04
tertiary granule CC 164 16 2.29E-07 2.93E-04
immune effector process BP 1139 52 2.45E-07 2.93E-04
cytoplasmic vesicle part CC 1462 67 2.49E-07 2.93E-04
leukocyte mediated immunity BP 762 39 2.58E-07 2.93E-04
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Abbreviations: GO; Gene Ontology, FDR; false discovery rate.

Table 2:

Result of the top 20 pathways of KEGG analysis with differentially methylated CpGs between delirium cases and non-delirium controls

Pathway N DE p-value FDR
Aldosterone synthesis and secretion 98 12 1.58E-04 0.038
Cholinergic synapse 112 13 2.28E-04 0.038
Long-term depression 60 8 0.001 0.086
Apelin signaling pathway 137 12 0.001 0.086
Parathyroid hormone synthesis, secretion and action 106 11 0.001 0.086
Pantothenate and CoA biosynthesis 19 4 0.002 0.088
Circadian entrainment 97 10 0.003 0.123
Fc gamma R-mediated phagocytosis 90 9 0.003 0.123
Gastric acid secretion 75 8 0.004 0.123
Melanogenesis 101 9 0.004 0.123
Sphingolipid signaling pathway 118 10 0.004 0.123
Serotonergic synapse 113 9 0.005 0.130
Adrenergic signaling in cardiomyocytes 144 11 0.007 0.166
Retrograde endocannabinoid signaling 141 10 0.007 0.166
Amoebiasis 94 8 0.008 0.166
Gap junction 88 8 0.008 0.167
GnRH signaling pathway 93 8 0.010 0.179
Pathogenic Escherichia coli infection 55 5 0.010 0.179
Leukocyte transendothelial migration 111 8 0.011 0.179
Cushing syndrome 155 11 0.011 0.179
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Abbreviations: KEGG; Kyoto Encyclopedia of Genes and Genomes, FDR; false discovery rate.

4. Discussion

This is the first study investigating the association between genome-wide DNAm and delirium in a series of patients with and without delirium. One CpG site at cg21295729 was genome-wide significantly correlated with delirium. In the top pathways based on over-representation analysis of GO terms, associations with immune response and inflammatory response were found. These results were consistent with our hypothesis that DNAm change on genes related to immune/inflammatory response can lead to hightened inflammation associated with delirium.

From genome-wide DNAm analysis, one genome-wide significant signal in the gene of LDLRAD4 was identified. LDLRAD4—low-density lipoprotein receptor class A domain containing 4—functions as a negative regulator of TGF-beta signaling that regulates the growth, differentiation, apoptosis, motility, and matrix protein production of a lot of cell types (Nakano et al., 2014). Although it was not genome-wide significant, the second top hit CpG was near the gene DAPK1. It is reported that DAPK1—death associated protein kinase 1—is induced by TNF-alpha and interferon-gamma (Yoo et al., 2012), and functions as regulating apotosis, autophagy and inflammation (Li et al., 2018). Although a potential role of these specific genes in pathophysiology of delirium requires further investigation, over-representation analysis identified several top pathways relevant to neurofunction and inflammatory/immune processes—including immune response, leukocyte activation, neutrophil activation, and myeloid cell activation from GO terms. These results emphasize the role of pro-inflammatory cytokines and neuroinflammation in the pathophysiology of delirium, consistent with our hypothesis. Furthermore, many pathways relevant to delirium and neural function were also identified from from KEGG terms, including cholinergic synapse, serotonergic synapse, and leukocyte transendothelial migration. The second top hit of the KEGG pathways was cholinergic synapse. The cholinergic system is one of the most important neurotransmitter systems in the brain, and deficiency of acetylcholine is well known to be associated with delirium (Hshieh et al., 2008; Maldonado, 2013). The results of the present study are further supporting the relevance of cholinergic function in potential pathophysiology of delirium. These findings from GO term and KEGG term relevant to delirium may support the validity of this epigenetic investigation of delirium pathophysiology.

We acknowledge that careful interpretation of these results is needed because variable confounding factors might have influenced the above results. For example, antipsychotics are often used for the treatment of patients with delirium (Oh et al., 2017), and antipsychotics are known to affect DNAm (Ovenden et al., 2018). Therefore, it is possible that antipsychotics had a potential effect for DNAm in the subjects of this study. However, there was only one delirium case and no control subject who was prescribed antipsychotics at the time of the blood sample collection. Thus, we were not able to assess the potential impact of antipsychotics on DNAm in our analysis.

We acknowledge several limitations of this study. First, the sample size is relatively small although this is the very first epigenetics study of delirium with genome-wide approach. To confirm the present findings, we need to increase the sample size for replication. Second, age distribution and the medical conditions that required subjects to be hospitalized were diverse among study subjects. Therefore, ages might have influenced the DNAm difference between delirium cases and controls. In addition, the present results are potentially confounded by various etiologies causing delirium among them. Comparing those with and without delirium after the same type of surgery (post-operative delirium) as well as the same characteristics including comorbidities would help minimize confounding factors. However, even with these limitations, the presented data showed supporting evidence of epigenetic differences in the immune/inflammatory response network and cholinergic system. Lastly, we used only blood samples and did not investigate brain tissues directly. However, there is a significant correlation between brain tissue and blood in DNAm levels, as shown in our previous study (Braun et al., 2019). Also, our data support the role of systemic inflammation leading to delirium. As the goal of our study is to identify potentially clinically useful biomarkers, we believe that investigating DNAm differences in blood associated with delirium is important to improve our future clinical practice.

In conclusion, this is the first epigenetics study of delirium. The DNAm was investigated genome-wide. The results were consistent with our previous work and hypothesis (Shinozaki et al., 2018a). Despite these limitations mentioned above, we showed evidence of epigenetic differences both at gene levels and network levels between delirium cases and non-delirium controls, supporting the role of immune/inflammatory response and cholinergic synapse. This finding indicates that DNAm status in blood may become a useful epigenetic biomarker for delirium.

Supplementary Material

Supplementary Table 3
Supplementary Table 1
Supplementary Table 2

Highlights.

  • A genome-wide significant CpG site near the gene of LDLRAD4 was identified.

  • Several significant pathways related to immune/inflammatory response were shown.

  • DNA methylation status in blood may be used as epigenetic biomarkers for delirium.

Role of the funding source

This study was funded by research grants from National Science Foundation1664364 as well as the National Institute of Mental Health (K23 MH107654).

Footnotes

Conflict of interest

Dr. Shinozaki G is co-founder of Predelix Medical LLC, and reports U.S. Provisional Patent Application No. 62/731599, titled “Epigenetic biomarker of delirium risk.” The other authors declare that they have no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 3
NIHMS1623058-supplement-Supplementary_Table_3.pdf (45.5KB, pdf)
Supplementary Table 1
NIHMS1623058-supplement-Supplementary_Table_1.pdf (38.4KB, pdf)
Supplementary Table 2
NIHMS1623058-supplement-Supplementary_Table_2.pdf (45.3KB, pdf)

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