Transcriptomic Analysis of the Entorhinal Regions of Symptomatic and Asymptomatic Alzheimer’s Patients Varun Ullanat1, Ryna Shireen Sheriff1 1RV College of Engineering, India Email: varunullanat.bt17@rvce.edu.in
ABSTRACT
RESULTS
Asymptomatic AD is a condition where patients have intact cognition but neuropathology similar to AD. Studying the genome-wide expression of transcripts in Asymptomatic AD brains could help us understand the molecular mechanisms that play a role in the onset of AD. In this study, microarray expression counts from the entorhinal region of the brain is taken from AD, Asymptomatic AD and Normal (control) patients from the GEO database. The gene expression of the three conditions are compared as three different cases: AD versus Asymptomatic AD, AD versus control and Asymptomatic AD versus control. Differential gene expression (DGE) and Co-expression is first done to analyse the abnormalities in the transcriptome. Overrepresentation analysis (ORA) and Gene set enrichment analysis (GSEA) is done to find out the significant pathways and modules. Finally, protein-protein interaction networks are constructed to identify potential network hubs for targeting. Neuronal pathways including postsynaptic signal transduction and transmission across synapses were affected in all cases, and some interesting pathways such as Cytokine signalling was found to be altered in the Asymptomatic AD versus control case.
INTRODUCTION • • • • • • •
I. AD versus Asym-AD P Value 3.78E-25 1.35E-24 1.10E-23 6.09E-23 1.28E-22
Gene HIPK3 CPT1A C4orf31 FTSJD1 LOC643424
II. AD versus normal
Alzheimer’s disease (AD) is a neurodegenerative disorder indicated by an accumulation of extracellular amyloid-β (Aβ) protein AD can be symptomatic or asymptomatic. Asymptomatic patients comprise approx. 20-30% of the aging population having Aβ accumulation but intact cognition. They can progress to symptomatic stages as well. Differential gene expression refers to the analysis and interpretation of differences in abundance of gene transcripts within a transcriptome Co-expression analysis identifies which genes tend to show a coordinated expression pattern across a group of samples. Gene set enrichment analysis is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins and may have an association with disease phenotypes. Over-representation analysis detects enrichment of genes within biological categories. Protein-protein interaction networks are mathematical representations of the physical contacts between proteins in the cell
EXPERIMENTAL DESIGN
Gene
P Value
FBLN1
6.66E-20
C4orf31
7.86E-20
FAT1
6.24E-19
C1QTNF5
8.84E-19
ECHDC3
8.98E-19
III. Asym-AD versus normal Gene CYBB TNFRSF14 PLEC1 ATOH8 METTL7A
Microarray Data
P Value 1.72E-08 1.41E-07 1.71E-07 3.05E-07 4.62E-07
Pre-processing
AD vs Asym
DGE
AD vs Control
DGE
Asym-AD vs Control
DGE
Co-expression
Co-expression
Co-expression
- ORA - GSEA - Protein interaction network
- ORA - GSEA - Protein interaction network
- ORA - GSEA - Protein interaction network
METHODS • All coding was done using the R software. The dataset used is GSE118553 from the GEO database. • First, the microarray data was pre-processed using the package beadarray by removing bad-quality probes and gene repeats. The probes are annotated with the gene names. • DGE was then done using the package Limma. A p-value cut-off of 0.05 was used for identifying significant differentially expressed genes. • Next, co-expression studies were conducted on the differentially expressed genes using the package CEMiTool. • GSEA was done using the fgsea package to find out enriched modules generated by the co-expression studies. • ORA for significant pathways was done with the CEMiTool package. The pathways and the genes involved in each is downloaded from the REACTOME database. • Finally, protein-protein interactions of the co-expressed, differentially expressed genes were incorporated using interaction data from the STRINGS database. • This is done for each case; i.e. AD vs control, Asymptomatic AD versus control and AD versus control, but in the Asymptomatic AD vs control case, no coexpression modules were found
CONCLUSION • The study is consistent with previous findings on genes involved in the onset of AD. Genes such as APOE4, FOS, SNAP25 and SDC4, which have been shown to have altered expression levels in AD patients, are highly pronounced in the current study results as well. The protein interaction networks sheds light on the interactions of these gene products, and could be useful in understanding inherent processes that contribute to AD onset. • In the Asymptomatic AD vs control case, the pathways such as Interferon gamma signalling, Interleukin 4 and Interleukin 13 signalling and co-stimulation by the CD28 family were all found to be altered suggesting a immunological aspect to early AD onset. References: [1] Patel et al., (2019). Transcriptomic analysis of probable asymptomatic and symptomatic Alzheimer brains. Brain, Behavior, and Immunity [2] Russo, et al., (2018). CEMiTool: A Bioconductor package for performing comprehensive modular co-expression analyses. BMC Bioinformatics.