May 31, 2024

Juan Valenciana

11th Grade

St. Francis Preparatory School

Brain cells, also known as neurons or glial cells, make up the structure of the nervous system. Astrocytes are a type of glial cell that provides energy for neurons, forms the blood-brain barrier which manages the passage of substances between the blood and the brain and contributes to synaptic connection in the brain. These functions of astrocytes in the CNS, including when astrocytes undergo physical and chemical changes, emphasize their importance in understanding brain health and disease progression. 

Neurodegenerative diseases (NDs) are conditions where the brain's cells gradually stop working properly. The most common types of NDs are Alzheimer's, Parkinson's, and Huntington's disease. Neuroinflammation is implicated in a variety of NDs. In short, it is the brain's response to injury or disease, which can be both protective and harmful. While a certain level of inflammation can be a normal response to protect the brain, prolonged inflammation leads to neuronal death and contributes to the progression of NDs. 

Astrocytes, being key responders to neuroinflammatory cues, are frequently associated with ND’s, but their role is not fully understood. Astrocytes become activated in response to neuroinflammation. This activation results in changes in their structure, including damage to the blood-brain barrier, which subsequently allows harmful substances into the brain. 

RNA profiles are snapshots of the activity in a cell and the gene expression that is occurring. Genes are instruction manuals stored in the DNA, telling the cells how to make proteins. Gene expression refers to the process by which the instructions in a gene are used to synthesize RNA molecules. Increasing levels of gene expression means that the cell uses the instructions from that gene more often to make RNA molecules. The novelty of this study is that an immunoprecipitated, astrocyte-targeted RNA profile is being used to determine exactly what's happening in astrocytes in the context of inflammation. 

In a previous study that this study is building off of, mice were injected with lipopolysaccharide (LPS) to cause neuroinflammation. Brain tissue samples were collected from mice post-injection, and RNA molecules were isolated using Immunoprecipitation (IP). In this study, antibodies targeting an HA tag, attached to ribosomes in astrocytes, were used to pull down the ribosome-bound RNA from astrocytes, allowing researchers to study gene expression specifically in astrocytes.

Over the past four months, code was developed in R (a programming language) to analyze RNA-seq data. Publicly available gene expression data from the previous study were collected, organized in a spreadsheet, and transferred into RStudio. Data cleaning was applied by filtering and removing lowly expressed genes. Clustering algorithms were utilized, and heat map visualizations were created to explore correlations between gene expression and the experimental conditions simulating neuroinflammation. These steps enabled the visualization of gene expression patterns at two time points, 1 and 14 days after the mice were injected with the drugs.

Figure 1 shows the gene expression levels of the astrocyte-specific tissue samples of mice. Each row represents a unique gene, and each column represents a mouse that was used in the study. Columns under the blue rectangles were mice injected with PBS or saline, while columns under the red rectangles were injected with LPS and had neuroinflammation simulated upon them. The Z score scale displays how much a particular gene’s expression level differs from the mean expression level across all genes in the dataset. The figure above is somewhat messy. While each row represents an individual gene, it is impossible to list every gene within a confined space.

To identify important individual genes that were upregulated and downregulated on the first day, volcano plots were created. 

Figure 2 highlights genes that are highly expressed and statistically significant from the whole tissue dataset. The x-axis represents log2 fold change: it indicates how many times more or less abundant a gene is expressed in one condition compared to another. Genes with a log2 fold change greater than 0.5 or less than –0.5 were considered significantly expressed. The y-axis represents the p-value or its statistical significance. Genes with a p-value of less than 0.1 were considered statistically significant. This also measures the probability that the result occurred by changes. Genes with a significant log2 fold change as well as a p-value less than 0.1 are highlighted in magenta.

GFAP is the most notable gene because it has been a common biomarker for astrocyte activation and is commonly associated with neuroinflammatory responses in various neurodegenerative diseases. 

Future implications of this study include utilizing Gene Set Variation Analysis (GSVA) to identify enriched biological pathways and processes associated with statistically significant genes. Additionally, understanding the functions of groups of upregulated and downregulated genes will reveal molecular mechanisms of astrocyte responses during neuroinflammation.