Arabidopsis Transcriptomic Responses to Simulated Microgravity

Skills:

Programmatically queried and processed transcriptomic datasets from NCBI GEO using GEOparse in Python
Built gene expression matrices, performed log2 normalization, and separated samples by experimental condition (simulated microgravity vs. 1g control)
Conducted differential expression analysis using fold change and t-tests, adapting for single-sample conditions
Developed volcano plots in Python to visualize the most significantly up- and downregulated genes under simulated microgravity
Interpreted transcriptomic trends to identify biological pathways sensitive to altered gravity

I used NCBI GEO (accession GSE14768) data for this project.

Dataset Used: NCBI GEO, accession GSE14768

The primary goal of this project was to investigate how Arabidopsis thaliana seedlings respond at the transcriptomic level to simulated microgravity on Earth, using a Random Positioning Machine (RPM) to mimic aspects of spaceflight conditions. Simulated microgravity environments minimize the directional effects of gravity, allowing researchers to explore how biological systems adapt to altered mechanical stress. This dataset was chosen because it includes both RPM-treated seedlings and 1g ground controls, providing a clear comparison for differential expression analysis.I first downloaded all six samples from GEO and built a gene-by-sample expression matrix. Samples were classified deterministically: GSM368810–GSM368812 represented simulated microgravity, while GSM368835–GSM368839 served as 1g controls. Expression values were converted to numeric type and log2-normalized to reduce skew from highly expressed genes and stabilize variance across the dataset.Because only one microgravity sample was available, I adapted the statistical analysis by comparing this single-sample distribution against the control group using a modified t-test. This approach allowed us to quantify which genes were significantly up- or downregulated despite the small sample size. Fold changes were calculated as log2 ratios between the microgravity and control means, enabling easy comparison of magnitude and direction of gene expression shifts.The resulting volcano plot illustrates the transcriptomic response (Figure 1). Most genes cluster near zero on the x-axis, indicating that the majority of transcripts are minimally affected by simulated microgravity. However, a subset of genes shows both large fold changes and high statistical significance. Notably, many of these highly responsive genes are downregulated, suggesting that specific pathways are suppressed under microgravity conditions. A smaller group of genes is upregulated, highlighting targeted activation in response to altered gravity.This analysis demonstrates that even short-term exposure to simulated microgravity can reshape the expression of specific genes rather than producing a global stress response. The top differentially expressed genes identified in this study may serve as candidates for further investigation into gravity-sensitive pathways, mechanotransduction, and plant adaptation in spaceflight environments.

Figure 1: Volcano plot of gene expression differences between Arabidopsis seedlings grown under simulated microgravity (Random Positioning Machine) and 1g controls. Each point represents a gene, with the x-axis showing log2 fold change (microgravity vs. 1g) and the y-axis showing –log10(p-value) from a modified differential expression test. Genes with large expression changes and high statistical significance appear toward the upper left and right of the plot. Most genes are minimally affected, while a small subset shows strong responses to simulated microgravity, predominantly downregulated.

Page updated

Google Sites

Report abuse