Transcriptional Changes of Genes Linked to Alzheimer’s Disease

Wanning He

Shenzhen College of International Education, Futian District, Shenzhen, China

Keywords: Alzheimer’s Disease, GWAS, Bulk RNA-Seq.

Abstract: Alzheimer’s disease is a pogressive neurodegenerative disease that constitutes most cases of dementia. This

study aims to converge existing data from GWAS studies and bulk RNA-Seq from patients with and without

Alzheimer’s disease to prioritize genes involved in the disease pathology. For this study, I examine existing

bulk RNA-Seq datasets from patients with and without Alzheimer’s disease [GSE159699], focusing on genes

previously identified as links to late-onset Alzheimer’s disease in GWAS studies. I confirmed my results with

a publicly available AD transcriptomics consensus tool published by the Swarup lab. In my analysis, I

identified shared gene expression differences in STAG3L5P, MEF2C, MS4A6A, PILRA and CASS4 between

Alzheimer’s disease and control patients across several datasets. These genes were previously linked to late-

onset Alzheimer’s disease. Further investigation should explore how their mutations and gene expression

differences contribute to the mechanisms underlying Alzheimer’s disease.

1 INTRODUCTION

Alzheimer’s disease (AD) was first diagnosed by

Alois Alzheimers in 1907 in a case of a 51-year-old

woman who was experiencing a relatively rapidly

deteriorating memory along with psychiatric

disturbances. Over time, the definition of AD has

changed, and today we consider it to be a neurological

disorder accompanied by a hallmark pathology:

presence of extracellular amyloid beta plaques and

intracellular neurofibrillary tangles formed of tau

protein in the brain (Matthews, Xu, Gaglioti, Holt,

Croft, Mack, McGuire. 2019); (Morgan, 2011).

Currently, AD affects more than 20 million people

worldwide, with about 135 million people expected

to develop it by 2050 (Castellani, Rolston, Smith.

2010); (Fratiglioni, Ronchi, Agüero-Torres. 1999).

AD can be classified into two categories, early

and late-onset, defined by the age of diagnosis and

inheritance pattern (Masters, Bateman, Blennow,

Rowe, Sperling, Cummings. 2015). Whilst early-

onset AD forms around 10% of the cases, around 90%

of AD cases are late-onset, with 85% of the patients

older than 75 years of age (Rabinovici. 2019).

Multiple genetic mutations are responsible for the

development of AD. Mutations in genes processing

amyloid beta proteins are linked to early-onset AD:

https://orcid.org/0000-0002-7955-7340

APP, PSEN1 and PSEN2 (Masters, Bateman,

Blennow, Rowe, Sperling, Cummings. 2015).

Genome-wide association studies (GWAS) have

identified a number of risk factors related to late-

onset AD, including the APOE allele e4 (Rabinovici.

2019). APOE e4 carriers have a higher risk of

developing late-onset AD, in contrast to carriers of e2

or e3 alleles. Subsequent GWAS studies have

identified dozens other loci conferring risk factors for

late-onset AD, including TREM2, ADAM10,

ADAMTS1 and others (Kunkle, 2019); (Jansen,

2019).

To understand more about the mechanism behind

the pathology of AD, I evaluated the expressions of

genes linked to late-onset AD in patients with and

without Alzheimer’s disease. I assessed whether the

genes that confer a known risk towards late-onset

Alzheimer’s disease are also differentially expressed

in patients with Alzheimer’s disease, regardless of

their mutation status.

2 METHODOLOGY

Publicly available bulk RNA-Seq datasets from post-

mortem temporal lobes from patients with and

without Alzheimer’s disease were used to investigate

1112

He, W.

Transcriptional Changes of Genes Linked to Alzheimer’s Disease.

DOI: 10.5220/0011378300003443

In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2022), pages 1112-1118

ISBN: 978-989-758-595-1

the expression of genes previously identified as

linked to late-onset AD [GSE159699, 9]. The data

was re-analyzed using DESeq2 in Rstudio (Michael,

2014). Six patients that were identified as outliers via

PCA were removed from the dataset, including three

patients with AD and three healthy controls. Using

boxplots, I investigated the expression of genes

previously identified as linked to late-onset AD

(Kunkle, 2019); (Jansen, 2019) in the GSE159699

dataset. To compare my results with previously

published literature, I investigated the expression of

the genes linked to late-onset AD via a publicly

available AD consensus transcriptomics resource

developed by the Swarup lab (Morabito, 2020) which

reanalyzed human AD gene-expression datasets from

several resources, including the ROSMAP dataset

(Bennett, 2018) and the Mayo dataset (Allen, 2016).

The main goal of this study was to investigate the

expression of genes previously identified as

associated with late-onset AD in GWAS studies, in

bulk RNA-Seq data from post-mortem brains with

and without Alzheimer’s disease. For this, I identified

genes of interest associated with late-onset AD

published in recent GWAS studies (Kunkle, 2019);

(Jansen, 2019). Using bulk RNA-Seq dataset from the

temporal lobes of these patients, I compared the

expression of the genes linked to late-onset AD in the

temporal lobes of patients with and without AD

[GSE159699, 9]. To relate my research to previously

published literature, I compared my results to the AD

consensus transcriptomics resources by the Swarup

lab (Nativio, 2020) which was used to display AD

consensus gene expression from multiple sources

(Bennett, 2018); (Allen, 2016).

The GSE159699 dataset is a bulk RNA-Seq

dataset from the temporal lobes of 12 patients with

AD, 10 healthy age-matched controls to the AD

patients and 8 healthy young controls (Nativio, 2020).

First, I plotted all patients using a PCA plot and a

heatmap of the top 20 differentially expressed genes

in DESeq2 (Michael, 2014). I identified 6 outliers,

that were significantly different from all the other

patients. After removing the outliers from the dataset,

the AD patients (n=9) were separated from the

healthy young (n=4) and healthy old controls (n=7) in

the PCA plot, with PC1 explaining 19% variance in

the dataset (Figure 1). A heatmap of the top 20

differentially expressed genes did not identify any

significant difference between AD patients and the

healthy and young controls. This result was

unremarkable and in line with previous results

published by Nativio et al. (2020), who reported

differences in expression among the genes related to

epigenetic alterations (Nativio, 2020). The original

paper by Nativio et al. (2020) analyzed gene

expression of only genes related to the GO term

‘regulation of transcription’ (Nativio, 2020), whereas

my heatmap graph presented the top 20 genes that

were differentially expressed among the AD, young

and old healthy controls (Figure 1).

Figure 1: PCA and heatmap of top 20 differentially

expressed genes among the bulk RNA-Seq data from the

temporal lobes of patients with and without AD

[GSE159699, 9].

Because genes that are differently expressed

between AD and controls were previously discussed

and reported in the original paper by Nativio et al.

(Nativio, 2020), I did not seek to replicate the analysis,

but instead decided to look into whether genes

previously identified as links to late-onset AD in

GWAS studies are differently expressed between AD

and controls in the GSE159699 bulk RNA-Seq

dataset which contained AD patients, along with old

and healthy controls. For this, I identified genes

linked to late-onset AD using previously published

GWAS studies (Kunkle, 2019); (Jansen, 2019). For

each gene previously identified as linked to late-onset

AD, I reported whether it was differently expressed in

the current dataset. In addition, I crossed my results

with the AD transcriptomics consensus resource

Transcriptional Changes of Genes Linked to Alzheimer’s Disease

1113

developed by the Swarup lab (Morabito, 2020). This

resource allows plotting of gene expression

differences among the brains of AD patients, age-

matched patients, and healthy controls. The plots

below summarize fold-change expressions between

AD and controls using the GSE159699 dataset

(Nativio, 2020), and the AD consensus resource by

the Swarup lab (Morabito, 2020).

Figure 2: The boxplot of differences in STAG3L5P-

PVRIG2P-PIL gene expression among AD, along with

healthy young and old controls indicates a significant

increase of expression of STAG3L5P - PVRIG2P - PIL in

the AD group. On the left is the boxplot of gene expression

differences plotted using the bulk RNA-Seq data from the

temporal lobes of patients with and without AD

[GSE159699, 9]. On the right is the boxplot of gene

expression differences in bulk RNA-Seq datasets using the

AD gene expression consensus resource developed by the

Swarup lab (Morabito, 2020). Three published datasets

show an upregulated expression while three shows a

downregulated expression.

The STAG3L5P gene displays an increase in

expression in AD vs control patients in the

GSE159699 dataset. When compared to the AD

consensus transcriptomics resource by the Swarup lab,

STAG3L5P also showed an increase in expression in

the ROSMAP dataset (Fig.7a). Previously, the

exome-sequencing studies showed that the

associations with two variants in a novel gene STAG3

were also replicated and significantly associated with

AD in the replication analysis. The rare variants in

STAG3 identified by WGS suggested the possibility

that STAG3 has a distinct mechanistic role in AD

which is different from other normal variants (Joshua,

2020).

Figure 3: The boxplot of differences in ME2FC gene

expression among AD patients, along with healthy young

and old controls indicates a significant decrease in ME2FC

of expression in the AD group. On the left is the boxplot of

gene expression differences plotted using the bulk RNA-

Seq data from the temporal lobes of patients with and

without AD [GSE159699, 9]. On the right is the boxplot of

gene expression differences in bulk RNA-Seq datasets

using the AD gene expression consensus resource

developed by the Swarup lab (Morabito, 2020).

A Similar process was performed for the MEF2C

gene, which showed a decrease in expression in AD

vs control patients in the GSE159699 dataset.

Comparison to the AD consensus resource by the

Swarup lab revealed unclear changes in gene

expression between AD patients and the controls,

displaying a decreased expression in AD brains using

the Mayo and the MSMM dataset, but not in the other

datasets (Figure 3). MEF2C has a role in conferring

resilience to pro-inflammatory stimuli in microglia

(Deczkowska, 2017). Microglia plays an important

role in AD, and MEF2C restricts the microbial

response to immune stimuli. Additionally, other

GWAS studies show that mutations in MEF2C are

linked to late-onset AD. Inflammation is known to be

associated withcognitive dysfunction and may

contribute to the pro-inflammatory milieu of the brain

in AD or aging patients (Simen, 2011).

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

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MS4A6A displays an increased expression in the

AD group using the GSE159699 dataset, Mayo and

MSSM dataset, but the gene expression relationship

is unclear using the other datasets from the AD

consensus resource by the Swarup lab (Nativio, 2020)

(Morabito, 2020). Previous studies show that

MS4A6A is associated with AD and is likely to have

an immune-related function (Reitz. 2015); (Paul,

2011).

Figure 4: The boxplot of differences in MS4A6A gene

expression among AD patients along with healthy young

and old controls indicates an increase in MS4A6A of

expression in the AD group. On the left is the boxplot of

gene expression differences plotted using the bulk RNA-

Seq data from the temporal lobes of patients with and

without AD [GSE159699, 9]. On the right is the boxplot of

gene expression differences in bulk RNA-Seq datasets

using the AD gene expression consensus resource

developed by the Swarup lab (Morabito, 2020).

The PILRA gene is associated with late-onset AD

and exhibits an increase in gene expression in the AD

vs control group in the GSE159699 dataset and the

MSSM datasets from the AD consensus resource by

the Swarup lab. Previous research revealed a

significant burden of PILRA variants in the exome-

wide burden analysis of AD (Patel, 2018).

Figure 5: The boxplot of differences in PILRA gene

expression among AD patients along with healthy young

and old controls indicates an increase in PILRA expression

in the AD group. On the left is the boxplot of gene

expression differences plotted using the bulk RNA-Seq data

from the temporal lobes of patients with and without AD

[GSE159699, 9]. On the right is the boxplot of gene

expression differences in bulk RNA-Seq datasets using the

AD gene expression consensus resource developed by the

Swarup lab (Morabito, 2020).

Figure 6: The boxplot of differences in CASS4 gene

expression among AD patients along with healthy young

and old controls indicates an increase in CASS4 expression

in the AD group. On the left is the boxplot of gene

expression differences plotted using the bulk RNA-Seq data

from the temporal lobes of patients with and without AD

[GSE159699, 9]. On the right is the boxplot of gene

expression differences in bulk RNA-Seq datasets using the

AD gene expression consensus resource developed by the

Swarup lab (Morabito, 2020).

The CASS4 gene exhibits an increase in

expression in AD patients vs the control group in the

GSE159699 dataset as well as the Mayo dataset using

the AD consensus resource from the Swarup lab.

CASS4 was previously found to be associated with

the amyloid, tau pathology, cytoskeletal function and

Transcriptional Changes of Genes Linked to Alzheimer’s Disease

1115

the axonal transport pathways identified in GWAS

studies (Reitz. 2015). It was found to retain the motifs

required for the interaction with PTK2B and to

contribute to the pathology of AD (Beck, 2014).

3 DISCUSSIONS

AD is a progressive neurodegenerative disease that

accounts for the most cases of dementia. This study

aimed to converge existing data from GWAS studies

and RNA-Seq to prioritize the high-risk genes for AD.

I found that 5 genes, STAG3L5P, MEF2C, MS4A6A,

PILRA and CASS4, were both linked to AD in the

GWAS studies and differentially expressed in the

RNA-Seq analysis between the AD and control

groups using the GSE159699 dataset (Nativio, 2020).

To validate my findings, I compared my results with

an AD transcriptomics consensus tool published by

the Swarup lab (Morabito, 2020), that reanalyzed

golden-standard bulk RNA-Seq datasets from AD

patients along with healthy old and young controls

using various datasets, including Mayo and

ROSMAP (Bennett, 2018); (Allen, 2016).

4 CONCLUSIONS

My analysis shows that STAG3L5P, MEF2C,

MS4A6A, PILRA and CASS4 exhibit changes in

expression between AD and control patients that are

fairly consistent across different datasets. These

results suggest that these genes could be particularly

important in AD. All these genes were previously

found to be associated with AD in GWAS studies

(Kunkle, 2019); (Jansen, 2019). In addition, their

gene functions are relevant to AD mechanisms.

STAG3LAP has several rare variants identified

through exome-wide analysis, with suspected distinct

mechanisms in causing AD (Joshua, 2020). MS4A6A

has a gene function associated with microglial

function and immunity (Reitz. 2015); (Hollingworth,

2011). The five genes explored in this study should

be further investigated to confirm their causality to

AD, and the role of the genetic variants and changes

in the expression of mechanisms leading to AD.

Further understanding of how STAG3L5P,

MEF2C, MS4A6A, PILRA and CASS4 contribute to

AD may be used for early detection, prevention and

drug development in AD.

ACKNOWLEDGEMENTS

If any, should be placed before the references section

without numbering.

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