Housekeeping genes (HKGs) are the foundational genes responsible for maintaining basic cellular functions, such as metabolism and structural integrity. These genes are theoretically expressed at constant levels in all cells, regardless of tissue type or experimental conditions. This assumption of consistent expression makes them useful in molecular biology, particularly in quantitative Polymerase Chain Reaction (qPCR). qPCR is the standard method used to measure gene expression, and the accurate selection of an HKG is necessary for obtaining reliable results.
The Role of Housekeeping Genes in qPCR Normalization
The primary function of a housekeeping gene in a qPCR experiment is to serve as an internal control for normalization. Gene expression analysis by qPCR is susceptible to technical variability, such as differences in the initial quantity and quality of the RNA extracted from samples.
Normalization with an HKG corrects for differences in the efficiency of the reverse transcription step, which converts RNA into complementary DNA (cDNA). It also accounts for errors like pipetting inaccuracies that affect the total amount of template cDNA added to each PCR reaction. By measuring a target gene’s expression relative to a stably expressed HKG, researchers can isolate and compare the true biological changes between samples.
This comparison is mathematically achieved using methods like the comparative \(C_t\) (\(2^{-\Delta\Delta C_t}\)) method. This approach calculates the difference in the cycle threshold (\(C_t\)) values between the target gene and the reference gene (\(\Delta C_t\)). The \(\Delta C_t\) value provides a stable baseline against which expression changes of the gene of interest are measured. The successful use of this method hinges entirely on the reference gene maintaining stable expression throughout all tested samples.
Criteria for Selecting Stable Reference Genes
The stability of a candidate reference gene must be tested for every specific experimental condition, as the assumption of universal constancy is frequently incorrect. An ideal HKG should exhibit minimal variation in expression across all tested samples, including different treatment groups, time points, and tissue types. This requires a validation phase where several candidate genes are screened statistically before the main experiment.
Specialized software algorithms are used to assess stability and provide quantitative metrics to rank the candidates. For instance, the geNorm algorithm calculates an expression stability value, the M-value, by determining a candidate gene’s average pairwise variation with all other potential reference genes. Genes with lower M-values are ranked as more stable, typically requiring an M-value below 1.5.
Another widely used tool, NormFinder, employs a model-based approach to estimate the variation of each reference gene, both within and between experimental groups. It provides a stability value where a smaller number signifies a more stably expressed gene. The BestKeeper program uses the raw \(C_t\) values to calculate the coefficient of variation (CV) and standard deviation (SD).
The geNorm software also performs a pairwise variation analysis (\(V_{n/n+1}\)) to determine the minimum number of HKGs required for accurate normalization. This analysis compares the normalization factor derived from \(n\) genes with the factor derived from \(n+1\) genes. If the resulting value is below the threshold of 0.15, no additional reference gene is needed for robust normalization. Using the geometric mean of multiple validated HKGs is recommended to ensure the most reliable normalization factor.
Common Examples and Context-Specific Variability
Many early studies relied on a few historical housekeeping genes, such as Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), Beta-Actin (ACTB), 18S ribosomal RNA (18S rRNA), and TATA-box binding protein (TBP). These were chosen because they are expressed at high levels in most cell types, but this usage often overlooked their potential for variability. Research has confirmed that the expression of these traditional HKGs can fluctuate significantly depending on the biological context.
For example, GAPDH expression can be unstable in cells experiencing hypoxia or in cancer studies because the protein it encodes is involved in glycolysis, a process often altered in these states. Similarly, 18S rRNA is sometimes avoided because it is transcribed by RNA polymerase I, while most target genes are transcribed by RNA polymerase II, making their regulation potentially discordant. Its high abundance can also complicate accurate quantification in some assays.
No single housekeeping gene is universally stable across all experimental conditions, tissues, or disease states. The stability of any HKG is entirely context-dependent, necessitating the validation step for every new experimental setup. Researchers select a panel of candidate genes based on prior literature or data and then use statistical algorithms to identify the best single gene or combination of multiple genes.
In complex studies involving different cell lines, tissues, or diverse treatments, normalization is enhanced by using the geometric mean of two or more validated reference genes. This practice minimizes the risk of introducing systematic bias that could lead to inaccurate conclusions about the target gene’s expression. The careful selection and validation of a suitable reference gene set is a requirement for generating trustworthy and reproducible qPCR data.