A drug, ergothioneine, may be an exciting potential treatment to Alzheimer's disease.

AJAS · 2022 Biochemistry

Stephanie Wang

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Overview

EGT Reduces A-Beta Injury by Attenuating Neuroinflammation and Oxidative Stress in silico β-Amyloid is a distinguishing feature of late-onset Alzheimer’s disease (LOAD). Previous research has shown that the amino acid ergothioneine (EGT) protects against β-amyloid induced neuronal injury in mice; however, the exact mechanisms by which EGT exerts its neuroprotective effects remain unclear. Although it has never been pursued as a LOAD treatment in humans, we propose that EGT is a promising LOAD drug due to its ability to cross the blood brain barrier and limited adverse effects. In this study, RNA-seq data was analyzed for LOAD patients and EGT-administered mice brain samples to determine potential EGT drug targets and perturbed biological pathways. Through differential gene expression analysis, we identified consequential genes with roles in the kinin-kallikrein cascade, regulating blood brain barrier permeability, and regulating lipid peroxidation. Pathway analysis identified three highly perturbed pathways that overlapped in the LOAD and EGT datasets, all of which were key regulators in processes regulating neuroinflammation and/or oxidative stress. Using a molecule activity prediction model, we predicted the molecules within these pathways that EGT likely targets, including inflammatory regulators and oxidative stress attenuators, accounting for its neuroprotective effects against downstream injury caused by β-amyloid. These results validate the antioxidant and anti-inflammatory functions of EGT. If in vivo experimentation yields clinical benefit, EGT could be a candidate medication for LOAD treatment.

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From the student

In March 2021, I was notified of my selection to the Research Science Institute. I began working with Dr. Alterovitz’s lab at Harvard Medical School in May 2021, where I chose to conduct research on aging-associated diseases and pathways. I conducted an extensive literature review of aging-associated diseases that had relation to ergothioneine, and neurodegenerative diseases seemed to be a major theme. Alzheimer’s disease, in particular, seemed to be a promising disease that ergothioneine could treat. Hence, I began to further explore the relationship between ergothioneine and Alzheimer’s disease.

For the data analysis aspect of procedural implementation, I learned how to code in R and Python to conduct exploratory bioinformatics analysis, how to use the iDEP interface, and how to navigate and conduct analysis in Qiagen IPA. Dr. Alterovitz provided me with the ergothioneine dataset. This dataset was previously conducted by researchers in his lab who administered varying doses of ergothioneine to mice brain samples and conducted RNA-sequencing to determine gene expression. This is one of the very few RNA-seq datasets that investigates the molecular impacts of ergothioneine.

There exists numerous RNA-seq datasets on Alzheimer’s disease. I used the NCBI Gene Expression Omnibus to scour through the existing datasets, but most were focused on assessing a particular demographic or included data from only one study. Hence, these datasets were lacking in external validity and would not suffice as the sole dataset that I used for the Alzheimer’s group. Later, I found a paper by Li et al. in 2015 that was the largest meta-analysis of gene expression data as of its publication that combined data from hundreds of Alzheimer’s patients. This meta-analysis would be perfect for my research project due to its wide scope and high external validity. So, I used the datasets from the meta-analysis for my Alzheimer’s group.

Before conducting my novel analysis, I read numerous research papers to solidify my understanding of the current frontiers of Alzheimer’s disease and ergothioneine’s functionalities. I conducted an extensive literature review that helped me make my hypothesis on inflammation and oxidative stress as major overlaps between Alzheimer’s disease pathology and ergothioneine functionality. I read many research papers on how to conduct bioinformatics analysis, found numerous online tools for bioinformatics analysis, and determined that iDEP and a software called Qiagen IPA would provide the greatest assistance to my analysis. My iDEP analysis allowed me to find three gene families involved in blood brain barrier permeability, lipid peroxidation, and inflammation, and my Qiagen IPA analysis uncovered three pathways (production of nitric oxide and reactive oxygen species in macrophages, acute phase response signaling, and LXR/RXR activation) involved in neuroinflammation and oxidative stress. Hence, these findings confirmed by hypothesis and improved the specificity of the known mechanisms of action by EGT in Alzheimer’s disease.

I presented my results at the RSI Symposium and competed at the Texas Junior Academy of Science, where I won first place in the Biochemistry/Microbiology category and 2nd place overall. I am excited to present my research at AJAS!