RNA-Seq Analysis of Human Lung Cancer Cell Line A549 Following GSE1 Knockdown: Differential Gene Expression and Pathway Analysis Against siRNA Controls
The A549 cell line is often used as a model for human lung adenocarcinoma, and thus, insights gained from studies using these cells can be valuable for understanding lung cancer biology. The gene of interest, GSE1, may play a crucial role in regulating various cellular functions. By knocking down GSE1 and observing the transcriptomic changes, the study aims to identify the downstream effects and pathways regulated by this gene. These could include pathways critical for cell growth, apoptosis, or other essential cellular functions. This can lead to better understanding of the role of GSE1 in lung cancer and may even identify potential therapeutic targets. The primary objectives of the experiment are: 1. To effectively knock down the expression of the GSE1 gene in A549 cells. 2. To perform a comprehensive transcriptomic analysis using RNA-Seq to identify differentially expressed genes (DEGs) post-GSE1 knockdown. 3. To perform pathway analyses to understand the functional implications of DEGs. 4. To construct interaction networks for identifying downstream targets and pathways influenced by GSE1. Experimental Workflow Overview 1. Cell Culture and Seeding: A549 cells were plated at a density of \(2 \times 10^5\) cells/well in 6-well plates and cultured for 24 hours to allow for adhesion and growth. 2. Transfection: After 24 hours, cells were transfected with GSE1 siRNA or negative control siRNA. 3. Post-Transfection Incubation: Cells were incubated for an additional 48 hours post-transfection. 4. RNA Extraction: Cells were washed twice with PBS, and total RNA was extracted using the TRIzol reagent. 5. Quality Control: The quality and quantity of the extracted RNA were assessed using a NanoDrop One Microvolume UV-Vis Spectrophotometer. 6. cDNA Library Construction: cDNA libraries were generated from 500 ng of total RNA using the TruSeq RNA Sample Preparation Kit. 7. RNA Sequencing: Libraries were sequenced on a HiSeq 4000 instrument, generating approximately 20 million single-end reads (50SE) per sample. 8. Data Pre-processing: Raw sequencing data were processed to remove low-quality reads and adapters. Clean reads were obtained for downstream analyses. 9. Alignment and Mapping: Clean reads were aligned to the human reference genome using STAR software, allowing up to two mismatches. 10. Differential Expression Analysis: DEGs were identified between siGSE1 and control samples using DESeq2, applying an adjusted p-value cut-off of 0.05 and a false discovery rate (FDR) of = 0.001. This structured approach allows for a comprehensive understanding of the role of GSE1 in A549 cells, potentially providing valuable insights into its role in lung cancer.