A differential expression analysis of chemokine receptor binding genes between RNA sequencing datasets of Alzheimer and non-Alzheimer patients

A differential expression analysis of chemokine receptor binding genes between RNA sequencing datasets of Alzheimer and non-Alzheimer patients

Kenny Cao

Normanhurst Boys High School

Abstract

Although Alzheimer’s disease (AD) is the current leading cause of dementia, and a multifactorial disease with hundreds of neuropathological pathways, there tends to be a focus on amyloid plaques with beta-amyloid depositions within many studies, rather than potential causes such as autoimmunity. Therefore, this paper aims to better understand the role of lesser-known pathways such as chemokine receptor binding genes in AD, and to identify future directions. CXCL10 and CXCR3, the most up-regulated and down-regulated genes respectively from the dataset, were also found to have elevated blood plasma concentration levels, along with higher cerebrospinal fluid (CSF) concentrations in individuals of earlier, mild forms of AD. This suggests future directions towards CXCR3 and its ligand CXCL10 being potential blood biomarkers, as well as potential targets for pharmaceutical intervention treatments due to low methylation detected in older patients. Therefore, this secondary sourced investigation broadens our knowledge of this neuropathological pathway and AD.

Literature review

Prevalence of AD

AD is a progressive neurodegenerative disease that affects over 55 million people globally – predominantly females 1. It is also the leading cause of dementia, the seventh main cause of death globally. According to the World Health Organisation (WHO), the number of individuals affected by dementia is estimated to nearly double to 78 million by 2030, and nearly triple to 139 million by 2050 2. According to the Australian Institute of Health and Welfare (AIHW), dementia now also causes the greatest burden of disease for any chronic illness amongst the elderly (>65) in Australia with an estimated 229,831 deaths, surpassing coronary heart disease at 219,780 (CHD) 3 .

1 World Health Organization, ‘Dementia’, in World Health Organization, 2023, [accessed 23 July 2023].

2 Ibid.

3 ‘Australian Burden of Disease Study 2022, Leading causes of disease burden’, in Australian Institute of Health and Welfare, 2022, .

AD is manifested by with gradual memory loss and cognitive decline due to damage of cholinergic neurons 4, and is characterised by various symptoms including withdrawal, confusion with time and place, and decreased judgement. There is also currently no complete cure or effective disease altering therapies for AD. Current therapy only offers symptomatic relief from AD, and the progressive loss of efficacy for these treatments also warrants the discovery of newer drugs through novel drug targets 5 Therefore, this creates a need for more research and knowledge into neuropathological pathways that contribute to AD. Despite this, neuropathological hallmarks of AD tend to be the focus of studies, with many investigating the presence of amyloid plaques with beta-amyloid depositions and intraneuronal hyperphosphorylated amyloid precursor protein (APP) tangles 6. Additionally, because AD is a multifactorial 7 disease with over 300 neuropathological pathways 8, this need for more holistic research is thereby justified through my investigation, which focuses on lesser known neuroinflammatory processes such as chemokine receptor binding genes that may also contribute to the pathogenesis of AD.

Chemokines

Chemokines are a large family of small (814kDa) proteins that are subdivided into four groups (CXC, CC, C, and CX3C) based on the relative position of two N-terminal residues of four conserved cysteines 9. These proteins help regulate leukocytes migrating in peripheral lymphatic organs 10. Although they play a vital role as signalling molecules in immune and nerve cells, an overexpression of chemokines can initiate a disruption of the integrity of the blood-brain barrier, thereby facilitating an infiltration from immune cells into the brain. This can cause adjacent glial cells such as astrocytes and microglia to release excess amounts of chemokines, leading to prolonged inflammation. This prolonged inflammation could also then degrade the protective role of the chemokines, increasing AB production and aggregation and impairment of its clearance, thereby contributing to neuronal loss and AD. However, because chemokines also play a role in regulating synaptic plasticity in regions responsible for memory and cognitive abilities 11, an underexpression, along with overexpression of chemokines also has the potential to contribute to the pathogenesis of AD.

4 J Wojcieszak, Katarzyna Kuczyńska & JB Zawilska, ‘Role of Chemokines in the Development and Progression of Alzheimer’s Disease’, in Journal of Molecular Neuroscience, vol. 72, 2022, 1929–1951,

5 AK Ramachandran et al., ‘Neurodegenerative Pathways in Alzheimer’s Disease: A Review’, in Current Neuropharmacology, vol. 19, 2021, 679–692.

6A Jorda et al., ‘The Role of Chemokines in Alzheimer’s Disease’, in Endocrine, Metabolic & Immune Disorders - Drug Targets, vol. 20, 2020, 1383–1390.

7 AK Ramachandran et al., ‘Neurodegenerative Pathways in Alzheimer’s Disease: A Review’, in Current Neuropharmacology, vol. 19, 2021, 679–692.

8 SL Morgan et al., ‘Most Pathways Can Be Related to the Pathogenesis of Alzheimer’s Disease’, in Frontiers in Aging Neuroscience, vol. 14, 2022,

9 J Wojcieszak, Katarzyna Kuczyńska & JB Zawilska, ‘Role of Chemokines in the Development and Progression of Alzheimer’s Disease’, in Journal of Molecular Neuroscience, vol. 72, 2022, 1929–1951,

10 O Koper et al., ‘CXCL9, CXCL10, CXCL11, and their receptor (CXCR3) in neuroinflammation and neurodegeneration’, in Advances in Clinical and Experimental Medicine, vol. 27, 2018, 849–856.

11 J Wojcieszak, Katarzyna Kuczyńska & JB Zawilska, ‘Role of Chemokines in the Development and Progression of Alzheimer’s Disease’, in Journal of Molecular Neuroscience, vol. 72, 2022, 1929–1951,

Research Methodologies

Many peer-reviewed studies conducted firsthand investigations such as Nativio et al. 12 and Koper et al. 13 which involved conducting proteomic analyses on frozen, post-mortem brains, as well as ELISA after determining chemokine concentrations in cerebrospinal fluid. However, since there is a limitation of resources, options for secondary research involve using primary datasets and using bioinformatic tools to digitally filter the dataset. This is also very common amongst many studies, which often perform chisquared tests of gene enrichment can also be used with databases such as DisGeNET 14, to reinforce findings within a specific dataset.

12 R Nativio et al., ‘An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease’, in Nature Genetics, vol. 52, 2020, 1024–1035, .

13 O Koper et al., ‘CXCL9, CXCL10, CXCL11, and their receptor (CXCR3) in neuroinflammation and neurodegeneration’, in Advances in Clinical and Experimental Medicine, vol. 27, 2018, 849–856.

14 J Piñero et al., ‘DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants’, in Nucleic Acids Research, vol. 45, 2016, D833–D839, .

Scientific research question

Is there a relationship between RNA expression levels for chemokine receptor genes involved in chemokine binding and Alzheimer’s disease (AD)?

Scientific hypotheses

H0:

There is no significant difference in the RNA expression levels between AD and non-AD patients for chemokine receptor genes involved in chemokine binding.

HA:

There is a significant difference in the RNA expression levels between AD and non-AD patients for chemokine receptor genes involved in chemokine binding.

Methodology

Raw single-end RNA-seq datasets were downloaded from the National Centre for Biotechnology Information (NCBI) from patients with Alzheimer’s disease (n=12) and healthy control samples (n=18) (accession no. GSE159699). The datasets were then uploaded into R Studio (v.4.2.2) 15 and trimmed using Trimmomatic (v.0.39) 16 to remove adapter sequences and poor-quality reads for accuracy, since they distort the alignment of reads to a reference genome. Subsequently, the trimmed RNA-seq reads were then aligned to a human reference genome (GRCh38) using STAR (v.2.1.3) 17 before the transcripts were counted and annotated with Stringtie (v.2.2.1). 18

15 ‘RStudio Desktop’, in Posit, < https://posit.co/download/rstudio-desktop/> [accessed 23 July 2023].

16 ‘USADELLAB.org - Trimmomatic: A flexible read trimming tool for Illumina NGS data’, in www.usadellab.org, [accessed 23 July 2023].

17 ‘Releases · alexdobin/STAR’, in GitHub, < https://github.com/alexdobin/STAR/releases> [accessed 23 July 2023].

18 ‘Releases · gpertea/stringtie’, in GitHub, < https://github.com/gpertea/stringtie/releases> [accessed 23 July 2023].

Because biological sex was still undetermined, datasets with high (>20 reads) expression of the XIST gene for biological females (XX), and Y chromosome genes for biological males (XY) were first identified. Afterwards, a hierarchal clustering of the XIST gene and Y chromosome genes was then performed to identify biological sex. EdgeR (v. 3.17) 19 was then used to create a DGEList data object containing the read counts for each gene as well as the sex and disease condition of each dataset. This dataset was then cleansed and filtered by removing genes with no expression. Subsequently, EdgeR was used again to perform a differential expression analysis using the likelihood ratio test (LRT) to create an output after modelling the data object against a negative binomial distribution model to account for the inherent errors within RNA-seq. This creates an output that includes a data frame containing log-fold changes (log2FC), log counts per million (log2CPM), likelihood ratio (LR), p-value and a false discovery rate (FDR) to identify false positives that p-value fails to account for. These values were then filtered in the differential expression analysis to determine specific genes which were differentially expressed (DEGs) through up-regulation (log2FC>0, FDR < 0.05) and down-regulation (log2FC<0, FDR<0.05).

ggplots (v.3.4.3) 20 was then used to construct a volcano plot for data visualisation, which graphs log2FC against -log10FDR. The transformation of -log10 was applied because so that lower FDR values would appear higher up and more significant. Afterwards, a gene enrichment analysis was then performed to assign the DEGs to known biological pathways to determine if these pathways were up-regulated or down-regulated in the diseased samples. This was done by comparing the dataset to the Reactome Gene Ontology (reactome.gmt). Furthermore, a chisquared test of enrichment was conducted after downloading a gene list from DisGeNET (disgenenet.AD.tsv) 21 to determine whether identified DEGs in the dataset were significant and supported by other studies, thereby ensuring reliability.

The dataset 22 was specifically chosen for this investigation because it used frozen postmortem (≤24h) brain samples, where members of the control group all had Braak and Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) scores of 0. This would ensure an accurate and valid investigation as the controls had an absence of amyloid plaques, neurofibril tangles, and other coincidental neurodegenerative diseases. Additionally, conducting a secondary sourced investigation minimises risks such as patient consent, and damage, aside from plagiarism, in which footnotes and relevant acknowledgements have already been included within this report. Furthermore, an FDR < 0.01, rather than FDR < 0.05, along with a filter of | log2FC | > 1 being used to increase the reliability of DEG expression, as only the most up-regulated and the most down-regulated gene for the pathway was chosen.

19 Y Chen et al., ‘edgeR: Empirical Analysis of Digital Gene Expression Data in R’, in Bioconductor, 2020, .

20 ‘Create Elegant Data Visualisations Using the Grammar of Graphics [R package ggplot2 version 3.2.1]’, in R-project.org, 2019, .

21 ‘DisGeNET - a database of gene-disease associations’, in www.disgenet.org, .

22 R Nativio et al., ‘An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease’, in Nature Genetics, vol. 52, 2020, 1024–1035, .

Additionally, peer-reviewed secondary sources filtered within the last five years from reputable databases such as PubMed, Google Scholar, and the NSW State Library were also used to supplement findings and draw further implications from the primary dataset. This furthers the reliability gathered from the chisquared test with DisGeNET 23 , and broadens knowledge regarding the neuropathological pathway of chemokine receptor binding and AD.

23DisGeNET - a database of gene-disease associations’, in www.disgenet.org, .

Results

Figure 1 – Contingency Tables for Chi-Squared Test (df = 1)

Of the 4701 differentially expressed genes, 590 were significantly enriched for association with AD (p-value = 5.3E-05; degrees of freedom = 1).

Figure 3 – Volcano Plot for FDR < 0.05

Figure 2 – Volcano Plot for FDR < 0.01

Volcano plots for DEGs. Blue represents DEGs while red represents non-DEGs. Upregulated genes have a log2 fold change > 0, while downregulated genes have a log2 fold change < 0. FDR, false discovery rate, is a scaled pvalue (calculated probability of the result appearing due to chance) that displays the ratio of the number of false positive results to the number of total positive test results. Higher -log10 FDR represents more statistically significant genes with lower false discovery rate.

Figure 4 – Box Plot A for FDR < 0.05

Figure 5 – Box Plot B for FDE < 0.01

Box plots representing scaled gene expression in scaled CPM (counts per million) for diseased and non-diseased individuals. Red represents individuals with AD (n=12, mean age = 68), while green and blue represent healthy, older (n=10, mean age = 68) and healthy, younger (n=8, mean age = 52) non-diseased individuals respectively.

Figure 6 – Table with Log2FC, log2CPM, LR, p-value, and FDR

Table representing raw, unscaled values for log2FC, log2CPM, LR, p-value, and FDR for CXCL10, ACKR2, CXCR1, CCL5, CXCR4, CXCR2, and CXCR3. LR, likelihood ratio, refers to the probability that the test result is expected in a patient with AD, compared to the likelihood that the same result would be expected in a patient without AD.

Discussion

Initially, the filter of FDR < 0.05 and | log2FC | > 1 yielded seven differentially expressed genes, where two genes were upregulated, and five genes were downregulated. CXCL10 was the most upregulated, with a log2FC of 2.34890824 (FDR=2.46E-05), while CXCR3 was the most downregulated, with a log2FC of -1.740075 (FDR=0.0015739). This is reflected through Figure 2, and because FDR < 0.05, the alternate hypothesis is accepted in favour of the null hypothesis, suggesting these DEGs are enriched and supported by studies in DisGeNET 24. Although FDR < 0.01 and | log2FC | > 1 was later applied, the most upregulated and downregulated genes remained the same, meaning that there was no gene with 0.01 < FDR < 0.05 that had the greatest | log2FC |, as shown in Figure 3.

Additionally, while data is available for log2CPM, which can measure gene expression, log2FC and FDR is primarily used, as the value of log2CPM is influenced by other various factors, along with the box and whisker plot seeming visually deceptive.

The upregulation of CXCL10 is significant because this gene normally functions as a pro-inflammatory chemokine responsible for cellular processes such as chemotaxis, differentiation, and the activation of peripheral immune cells, along with regulation of cell growth. 25 Although expression of CXCL10 is essential for homeostasis, Koper et al. 26 also reveals that an up-regulation of CXCL10 can elicit apoptosis in fetal neurons, due to CXCL10 elevating levels of intracellular of Ca2+. It suggests that this increased Ca2+ can trigger cytochrome c release, which can initiate the active capase-9, and in turn trigger apoptosis since the Ca2+ becomes available for uptake by the mitochondria associated with cytochrome release 27. These results may also provide targets for future directions such as pharmaceutical intervention to inhibit this process of apoptosis.

24 DisGeNET - a database of gene-disease associations’, in www.disgenet.org, .

25‘P02778 · CXL10_HUMAN’, in www.uniprot.org, [accessed 20 August 2023].

26 O Koper et al., ‘CXCL9, CXCL10, CXCL11, and their receptor (CXCR3) in neuroinflammation and neurodegeneration’, in Advances in Clinical and Experimental Medicine, vol. 27, 2018, 849–856.

27 Ibid.

Moreover, the up-regulation of CXCL10 is further supported by Zhou et al. 28 which revealed that plasma, or blood serum levels of CXCL10 were also significantly elevated in patients with AD with a large effect size (average ratio, 1.92; 95% CI, 1.03-3-58, p = 0.039; F- = 99.4%) in 78 AD and 64 controls. Hence, this suggests future directions for CXCL10 as a potential biomarker for AD. Furthermore, this also corresponds with findings from Bradburn et al. 29 who found that methylation, an epigenetic marker which controls transcription in polypeptide synthesis was significantly lower in the proximal promoter region of CXCL10 in the blood of older adults compared with younger adults. This lack of regulation would thus increase CXCL10 expression, aligning with the upregulation of CXCL10 in the dataset. However, after examining prefrontal cortex samples, the study also found higher CXCL10 protein levels within cortexes of those showing intermediate pathological signs of AD compared with aged controls, suggesting that CXCL10 may interestingly play a role in earlier AD stages as well. This is also further reinforced by initial study by Galimberti et al. (2003) 30 which revealed that CXCL10 concentrations in AD patients (n=36), as well as individuals with mild cognitive impairment (MCI) subjects (n=38), were significantly higher than controls (n=41), reinforcing the suggestion that chemokine plays a role in the early stages of AD, where inflammation may also be more pronounced. This is also reflected through Wojcieszak et al. 31 reporting higher concentrations of CXCL10 in patients with mild AD, but not those presenting severe AD, who have a Mini-Mental State Examination (MMSE) score < 15. In Galimberti et al. 2003 32, this increased concentration is reflected through the CXCK19 levels in CSF tested by ELISA in AD and MCI patients having a concentration of 103pg/mL and 128pg/mL respectively, in comparison to 69pg/mL within control groups. However, a similar study by Galimberti et al 33. three years later did not seem to reinforce these findings, as AD (n=48) and MCI (n=36) patients had a concentration of 108pg/mL and 121mg/mL respectively, while the control groups (n=29) had a concentration of 103pg/mL, suggesting an insignificant difference. While these findings still align with the correlation between CXCL10 expression and age in Figure 4 and Figure 5, more research is required to investigate its role in early AD stages. Similarly, although studies such as Corrêa et al. 34 established a positive correlation between CXCL10 and the presence of βamyloid in AD patients, there was no significant difference found in CXCL10 concentration between the two groups. Ultimately, whilst it cannot be concluded as to whether CXCL10 levels were different in the early stages of AD, it is still worth further investigating.

28 F Zhou et al., ‘Blood and CSF chemokines in Alzheimer’s disease and mild cognitive impairment: a systematic review and metaanalysis’, in Alzheimer’s Research & Therapy, vol. 15, 2023.

29 S Bradburn et al., ‘Dysregulation of C-X-C motif ligand 10 during aging and association with cognitive performance’, in Neurobiology of Aging, vol. 63, 2018, 54–64, [accessed 20 August 2023].

30 D Galimberti et al., ‘Intrathecal Chemokine Synthesis in Mild Cognitive Impairment and Alzheimer Disease’, in Archives of Neurology, vol. 63, 2006, 538.

31 J Wojcieszak, Katarzyna Kuczyńska & JB Zawilska, ‘Role of Chemokines in the Development and Progression of Alzheimer’s Disease’, in Journal of Molecular Neuroscience, vol. 72, 2022, 1929–1951, [accessed 20 August 2023].

32 D Galimberti et al., ‘Intrathecal Chemokine Synthesis in Mild Cognitive Impairment and Alzheimer Disease’, in Archives of Neurology, vol. 63, 2006, 538.

33 Ibid.

34 JD Corrêa et al., ‘Chemokines in CSF of Alzheimer’s disease patients’, in Arquivos de Neuro-Psiquiatria, vol. 69, 2011, 455–459.

Conversely, the downregulation of CXCR3 is also significant because CXCR3 serves as a receptor for chemokines such as CXCL10 and is responsible for mediating inhibitory activities on the proliferation, survival, and angiogenic activity of human microvascular endothelial cells (HMVEC). 35 Although the dataset suggests an inverse relationship between CXCL10 up-regulation and CXCR3 down-regulation, studies such as Xia et al. do not necessarily correspond with the dataset, as CXCR3 expression level remained unaltered, while its ligand CXCL10 was found to be up-regulated in astrocytes. However, this study does suggest a potential link between the CXCL10-CXCR3 axis in cell-cell communication between neurons, microglia, and astrocytes, making CXCR3 another biomarker worth investigating in conjunction with CXCL10.

Ultimately, the dataset used selected brain samples based on Braak and Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) scores, where control samples had scores of 0. This contributed to the accuracy and validity of the investigation, as a score of 0 suggests an absence of amyloid plaques, neurofibril tangles, and other coincidental neurodegenerative diseases, which may influence the results. Furthermore, the pvalue of 5.3E-05 yielded for the DisGeNET chi-squared test also suggested significant enrichment of these genes, furthering the reliability of the investigation. Additionally, as FDR accounts for false positives unlike pvalue, it is therefore more suitable to use, in conjunction with log2FC.

However, this methodology also presents limitations as the sample space of the primary dataset comprised of thirty individuals who were predominantly males. This is a limitation that may compromise the validity of the results because the data found fail to account for sex differences associated with AD.

Moreover, this small sample space of thirty individuals also means that the results are not representative of the multifactorial nature of AD, due to a possibility of skewed data.

Additionally, while log2FC detects upregulation and down-regulation, it also has limitations, since detected DEGs may not neccesarily have the greatest impact on AD, but rather, are just the most sensitive.

Although biomarker panels are available for AD, with high predictive values of up to AUC=0.84 36, they only apply for specific genes and references. Therefore, a future direction to consider is simultaneously balancing research into specific earlier treatments, while also ensuring early detection. However, in terms of prioritising early detection and early treatment, early treatment should be ensured, as early detection is redundant without adequate treatment. Additionally, AD testing also commonly involves transgenic mice, and it is estimated that approximately 99.6% of AD drugs that succeed in animal experiments fail in humans. 37 Hence, this raises further environmental and thus ethical considerations such as patient consent, and unknown side effects or treatment hazards.

36 A Hardy-Sosa et al., ‘Diagnostic Accuracy of Blood-Based Biomarker Panels: A Systematic Review’, in Frontiers in Aging Neuroscience, vol. 14, 2022.

37‘Alzheimer’s Disease Research Without Animals’, in Physicians Committee for Responsible Medicine, .

Conclusion

From the filter of FDR < 0.01, and | log2FC | > 1, five genes were found to be differentially expressed and enriched in DisGeNET for the pathway for chemokine receptor binding genes: CXCR4, ACKR2, CXCR2, CXCR3, and CXCL10. CXCL10 and CXCR3, the most upregulated and down-regulated genes respectively, were found to have elevated blood plasma concentration levels, along with higher CSF concentrations in individuals of earlier, mild forms of AD. This suggests future directions towards CXCR3 and its ligand CXCL10 being potential blood biomarkers, as well as potential targets for pharmaceutical intervention treatments due to low methylation detected in older patients. Therefore, an investigation of these genes and the specific pathway furthers our understanding of AD.

Acknowledgements

I would like to firstly acknowledge my Science Extension Teacher Ms Ritu Bhamra, for her guidance on research direction, data presentation, and topic refinement. Additionally, I would also like to acknowledge the mentors that accompanied UNSW SciX Genies: PhD researchers Lachlan Gray, Laurene Lelerc, Isabelle Greco, and Sophie Debs, (especially Lachlan Gray) for their assistance in coding, clarification of content, and research direction. Finally, I would also like to acknowledge my fellow classmates of 12SCIX1, and my fellow peers within and outside of UNSW SciX, who provided valuable contributions through class discussion in person, but also online through Microsoft Teams.

Reference list

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2. ‘Australian Burden of Disease Study 2022, Leading causes of disease burden’. in Australian Institute of Health and Welfare, 2022, .

3. Bradburn, S, J McPhee, L Bagley, M Carroll, M Slevin, N Al-Shanti, et al., ‘Dysregulation of C-X-C motif ligand 10 during aging and association with cognitive performance’. in Neurobiology of Aging, 63, 2018, 54–64, [accessed 26 November 2021].

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8. Galimberti, D, N Schoonenboom, P Scheltens, C Fenoglio, F Bouwman, E Venturelli, et al., ‘Intrathecal Chemokine Synthesis in Mild Cognitive Impairment and Alzheimer Disease’. in Archives of Neurology, 63, 2006, 538.

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Appendices

The code for this investigation can be accessed in the following repository: https://github.com/LGGray/SciX