How newly identified stable reference genes are revolutionizing Fc-fusion protein production for biopharmaceuticals
Behind every cutting-edge biologic medicine—from advanced cancer treatments to innovative autoimmune therapies—lies a sophisticated manufacturing process that depends on Chinese hamster ovary (CHO) cells.
Crucial for optimizing biopharmaceutical production processes
These unassuming cells have become the unsung workhorses of the biopharmaceutical industry, responsible for producing approximately 70% of all therapeutic proteins, including the vital Fc-fusion proteins that are transforming patient treatments worldwide 8 .
Reference genes serve as constant landmarks in a constantly changing cellular landscape, allowing researchers to distinguish meaningful genetic changes from background noise in gene expression studies.
For decades, scientists have relied on "housekeeping genes"—genes assumed to maintain constant expression levels because they perform basic cellular functions necessary for survival.
Genes like Actb (β-actin), which provides structural support to cells, and Gapdh, involved in energy production, have served as default reference points in thousands of studies.
However, emerging research has revealed these genes can vary their activity levels depending on cellular conditions, metabolic states, or environmental stresses 1 .
Different biological experiments create different cellular environments, and a gene stable in one context may fluctuate in another.
Consider CHO cells producing Fc-fusion proteins—these complex molecules combine a therapeutic protein with the Fc region of an antibody, creating enhanced stability and longer half-life in the bloodstream 3 .
Furthermore, biomanufacturing involves both fed-batch cultures and long-term cultivation, each creating distinct cellular stresses that can influence gene expression 1 .
Through rigorous testing across 26 different culture conditions, researchers have identified four exceptionally stable reference genes specifically suited for CHO cells producing Fc-fusion proteins 1 6 .
| Gene Symbol | Full Name | Primary Function | Stability Context |
|---|---|---|---|
| Akr1a1 | Aldo-keto reductase family 1 member A1 | Detoxification and metabolism | Top-ranked in both long-term and fed-batch cultures |
| Gpx1 | Glutathione peroxidase 1 | Antioxidant defense | High stability in long-term cultivation |
| Aprt | Adenine phosphoribosyltransferase | Purine recycling | High stability in long-term cultivation |
| Rps16 | Ribosomal protein S16 | Protein synthesis | High stability in fed-batch culture |
What makes these genes particularly interesting is their diverse cellular functions. Unlike traditional reference genes that often participate in similar cellular processes, these newly validated genes represent distinct biological pathways.
This functional diversity may contribute to their stability—when one pathway is affected by experimental conditions, others might remain unchanged.
To identify truly reliable reference genes, researchers designed an exhaustive study that mirrored real-world bioproduction scenarios.
The research team evaluated 20 candidate reference genes, selected through three strategic approaches:
| Experimental Aspect | Specific Conditions | Number of Conditions |
|---|---|---|
| Cell Lines | CHO-host + 5 GLP1-Fc producing lines (CHO-12, CHO-16, CHO-39, CHO-40, CHO-69) | 6 lines |
| Culture Methods | Long-term (75-day) passage + Fed-batch (14-day) culture | 2 systems |
| Sampling Points | Various phases: lag, log, stationary | 26 total conditions |
| Candidate Genes | Traditional HKGs + Literature-reported genes + RNA-seq candidates | 20 genes |
Determining which genes are truly stable requires more than just visual inspection of data. Researchers employed four independent statistical algorithms to assess expression stability 1 .
Identifies the most stable genes by comparing pairwise variation between all candidate genes.
Uses a model-based approach that estimates both intra-group and inter-group variation.
Relies on raw cycle threshold (Ct) values and calculates stability based on pairwise correlations.
Compares the relative expression of pairs of genes under different conditions.
The ultimate test for any proposed reference gene comes through practical application. To validate their findings, researchers used the identified reference genes to measure expression of the GLP1-Fc fusion protein itself 1 .
Absolute RT-qPCR
Exact copy numbersConfocal Microscopy
Visual protein confirmation| Gene | Stability Ranking (Long-term Culture) | Stability Ranking (Fed-batch Culture) | Overall Assessment |
|---|---|---|---|
| Akr1a1 | 1 | 1 | Most stable |
| Gpx1 | 2 | 4 | Highly stable |
| Aprt | 3 | 5 | Highly stable |
| Rps16 | 6 | 2 | Highly stable |
| Actb | 18 | 17 | Least stable |
| Pabpn1 | 19 | 19 | Least stable |
Advancements in scientific discovery depend on both innovative thinking and specialized tools. The identification of reliable reference genes leveraged a sophisticated array of technologies and reagents.
| Tool/Reagent | Function in Research | Specific Example/Application |
|---|---|---|
| RT-qPCR Systems | Measures gene expression levels | Quantifying candidate reference gene expression across 26 conditions |
| RNA-seq Transcriptomics | Comprehensive gene expression profiling | Identifying new candidate reference genes from CHO cell databases |
| Statistical Algorithms | Assessing gene expression stability | geNorm, NormFinder, BestKeeper, and ΔCt method comparison |
| Cell Culture Systems | Maintaining CHO cells under controlled conditions | Long-term passage and fed-batch culture models |
| Validation Technologies | Confirming research findings | Absolute RT-qPCR and confocal microscopy for protein detection |
This toolkit represents the convergence of biological techniques, computational methods, and analytical technologies that enable modern biopharmaceutical research.
Each component plays a distinct yet interconnected role in moving from biological questions to reliable answers.
The identification of stable reference genes represents more than just a methodological improvement—it has tangible implications for biopharmaceutical development and production.
The discovery of these reference genes opens new avenues for CHO cell research and engineering.
The meticulous work to identify reliable reference genes in CHO cells illustrates a fundamental principle of science: progress often depends on getting the fundamentals right.
These newly validated genes—Akr1a1, Gpx1, Aprt, and Rps16—may not directly cure diseases or produce therapeutics themselves, but they empower the research that does. They represent the unseen infrastructure of discovery, enabling scientists to ask more precise questions and obtain more reliable answers.
As biotechnology continues to push boundaries toward more sophisticated treatments, including personalized medicines and novel fusion proteins, these fundamental tools will ensure that each advancement rests on a foundation of rigorous, reproducible science.
References will be listed here in the final publication.