Detailed Study Notes on qRT-PCR and Gene Expression Analysis Pt 2

qRT-PCR Overview

• Purpose: Evaluate gene expression in disease models using quantitative reverse transcription PCR (qRT-PCR).

• Context: Presented by Dr. Enda Clinton, lecturer at Dundalk Institute of Technology in the course PHARS8033, focused on Genomics and Bioinformatics.

Bioinformatics Tools

• Role: Utilize online databases like NCBI and literature to identify genes related to specific diseases.

• Process:

  • Identify mRNA nucleotide sequences for the genes of interest.

  • Use sequence data to design primers for each gene.

• Considerations in Primer Design:

  • Self-annealing properties must be avoided.

  • Tools for optimization:

  • Tm (melting temperature).

  • Hairpin prediction.

  • GC content analysis.

Genes and Primers

• Approach: Gene expression analysis provides a snapshot of mRNA levels for specific genes in disease states.

• Characterization: Identification of causal and symptomatic gene characteristics in various conditions, allowing for tracking disease model responses to treatments via transcriptional changes.

Molecular Biology Breakdown

• Key Concepts:

  • Transcription: The process where DNA is converted to mRNA.

  • Translation: The synthesis of proteins from mRNA at the ribosome.

• Terminology:

  • DNA: Carries extensive genetic information, often regarded as the "source code of life."

  • mRNA: The data carrier conveying specific instructions for protein synthesis.

  • Proteins: Functional molecules vital for cellular processes, including antibodies, hormones, and enzymes.

• Body's Capabilities: The human body has the innate ability to produce its own medicines when provided with accurate information from genes.

Genes Implicated in Cardiovascular Disease (CVD)

• Gene Expression Profiles: Well-characterized profiles exist for many diseases, including CVD, which is associated with specific genes.

Gene Data for CVD

• Table of Key Genes:

  • Includes Entrez IDs, Gene Names, and GO terms (Gene Ontology terms corresponding to biological processes and molecular functions).

  • Example Genes:

  • ABL1: Gene ID 25 (Intracellular signaling cascade, Signal transducer activity).

  • EGFR: Gene ID 10014 (Intracellular signaling cascade).

• Associated Diseases:

  • Includes conditions like Atherosclerosis, Heart Failure, etc.

mRNA Levels in Symptomatic Genes (Fibrosis)

• Measurement: Relative mRNA levels of genes involved in fibrosis are presented as fold change values.

  • Examples:

  • α-SMA

  • Col1A1

  • Col3A1

  • Col4A1

  • Lox2

  • PDGF B

Housekeeping Genes

• Importance: Essential for generating high-quality, reliable data in qRT-PCR experiments.

• Function: Serve as reference genes to normalize data and ensure reduction of error variations from samples.

• Sources of Variability:

  • Extraction differences.

  • RNA quality variations.

  • cDNA synthesis efficiency.

  • Differences in experimental samples.

• Characteristics of Good Housekeeping Genes:

  • Should be stable and consistently expressed across conditions and tissues.

• Examples:

  • Beta Actin, GAPDH, 18S ribosomal RNA.

Expanding Experimental Design

• Next Steps: After identifying the model and relevant genes, execute the experiment.

  • Variables: Introduce independent variables and organize experimental design into control and treatment groups.

mRNA Extraction Protocol

• Sample Preparation:

  • Use Trizol and Chloroform.

  • Collect samples from various sources (FFPE, blood, tissues).

• Incubation and Centrifugation: Sequential incubations and centrifuge steps for mRNA extraction.

  • Include small RNA enrichment methods.

Primer Design and cDNA Synthesis

• Primers: Sequence-specific primers crucial for the reaction.

• Methods:

  • One-Step RT-qPCR vs. Two-Step RT-qPCR.

• Key Components:

  • Reverse Transcriptase, DNA Polymerase, buffer systems, and dNTPs.

Data Quantification of Gene Expression

• Understanding Data: qPCR produces Ct values which are essential for quantifying gene expression.

• Requisites: Ensure consistent input of RNA/cDNA across all reactions for reliability.

Normalization Techniques

• Delta CT (ΔCT): Formula for normalizing gene expression across samples:

  • $<br>ΔCT = CT(Gene ext{ of Interest}) - CT(Housekeeping ext{ Gene})<br>$

  • Example: For values $25$ and $20$, $ΔCT$ results in $5$.

  • Interpretation: Each cycle represents a doubling; thus a difference of 5 yields a 32-fold expression difference (computed as $2^5$).

Advanced Statistical Analysis

• Delta-Delta CT (ΔΔCT): Comparison of treated versus control samples for fold changes:

  • $<br>ΔΔCT = ΔCT(Treated ext{ Sample}) - ΔCT(Control ext{ Sample})<br>$

  • Fold Change Calculation:

  • $ext{Fold Change} = 2^{(-ΔΔCT)}$; where a fold change of 4 indicates a 4-fold increase in expression.

  • A ΔΔCT of +2 results in a 75% reduction of gene expression (interpreted as a fold change of $0.25$).

Assumptions for qPCR Analysis

• Efficiency Considerations:

  • Assume 100% efficiency in PCR amplification, with practical tolerances of 90-110%.

• Housekeeping Gene Stability: Reference gene must remain stable across varying experimental conditions to validate results.

• Amplification Efficiency Equivalence: The efficiencies of both the target and housekeeping genes should closely match for accurate interpretations.