Why and How mRNA-Protein Ratio Matter
First shared here. I have been planning several posts, yet not ready for any of them. I decided to use one of AI tools, Write For Me, for the first time to write this blog post. I gave the relevant links and bullet points to the ChatGPT prompt, then asked for a 1000-word blog post. I need to give feedback and do revisions (and make it shorter later) as well. However, we finally did it. I will share the initial prompt, just in case you are wondering. Let’s see how it worked!
The accurate interpretation of mRNA and protein levels is a critical challenge in modern biology. While mRNA serves as a blueprint for protein synthesis, the correlation between mRNA and protein abundance is only moderate, complicating the design of translation-dependent experiments. Insights from multiple studies show that relying solely on mRNA data can lead to misleading conclusions, particularly in complex biological contexts like tissue specificity and disease research.
Why mRNA-Protein Correlation Is Limited
mRNA is often used to estimate protein levels, but studies show that this relationship is far from perfect. The analysis of 32 tissues revealed a correlation of 0.46 between mRNA and protein, suggesting that mRNA explains only a part of protein abundance. Measurement accuracy significantly influences the observed correlations. For example, in tumor proteomics, highly reproducible datasets showed better mRNA-protein alignment, emphasizing the need for rigorous validation of omics data.
Biological factors also complicate the relationship. Protein turnover and regulatory mechanisms such as translation initiation, mRNA stability, and post-translational modifications all contribute to discrepancies between mRNA and protein levels. Tissue/condition-specific phosphorylation further adds complexity, enabling proteins to adapt to distinct cellular environments independent of mRNA.
Key Factors Affecting mRNA-Protein Disparities
mRNA and Protein Turnover
Differences in the degradation and synthesis rates of mRNA and proteins can lead to inconsistent levels. While some proteins have long half-lives, their mRNA may be rapidly degraded, and vice versa. This creates a disconnect that highlights the limitations of using mRNA as a sole proxy for protein levels.
Tissue and Condition Specificity
Gene expression varies significantly between tissues and under different conditions. Proteins may be more abundant in certain tissues due to context-specific translation, while their mRNA levels remain stable. A study found that tissue-specific phosphorylation allows for fine-tuning of protein function, influencing the interpretation of gene expression. For instance, phosphorylated proteins tend to be expressed more ubiquitously, while non-phosphorylated forms are often tissue-specific, fine-tuning the cell’s performance to local needs. This complexity underscores the importance of integrating proteomic data with transcriptomic data for a comprehensive understanding.
Translation Efficiency and Post-Translational Modifications
Regulatory elements like microRNAs and RNA-binding proteins can affect how efficiently mRNA is translated into proteins. Additionally, modifications like phosphorylation alter protein function, adding complexity to the interpretation of gene expression data.
Protein Mass and Cell Size
Larger cells generally contain more protein mass, which impacts absolute protein levels. While this does not directly affect the mRNA-protein ratio, it can influence the interpretation of experimental data, particularly when comparing across different cell types. In some cases, histone levels are used as a normalization factor because they correlate with DNA content rather than cell size, offering a more stable baseline for comparison (also known as “proteomics ruler)”.
Improving Experimental Design and Data Interpretation
To overcome the challenges of mRNA-protein disparities, researchers are increasingly turning to improved reproducibility, intra-sample instead of inter-sample RNA-protein correlations, considering context specificity and integrated omics approaches.
Visualization tools like GeneRanger provide context-specific insights, allowing scientists to assess the translation potential of genes in specific tissues. The Human Protein Atlas and other specialized databases offer comprehensive datasets that enhance the reliability of gene expression analysis.
Conclusion
The mRNA-protein relationship is complex, with numerous factors affecting protein abundance beyond transcription. For accurate experimental design, especially in functional genomics and disease research, integrating transcriptomic and proteomic data is essential. By utilizing context-specific datasets and considering the biological and technical factors that influence gene expression, scientists can make more reliable predictions and better understand cellular behavior.
As you see below, I have already made a good selection of resources and summaries for it to organize the content. So, I am not sure whether it saved my time or not.
References/ Prompt from my Summaries and Selections
Protein vs mRNA levels, why need to be careful while translation-dependent experiment designs. Why I am obsessed with mRNA vs protein level disparities:
- The utility of protein and mRNA correlation, https://pdf.sciencedirectassets.com/271294/1-s2.0-S0968000414X00137/1-s2.0-S09680004140020[…]uY29t&ua=13035903555703095005&rr=8e16af2afed09552&cc=ie
- Insights into the regulation of protein abundance from proteomic and transcriptomic analyses, https://www.nature.com/articles/nrg3185.pdf?utm_source=sciencedirect_contenthosting&getft_integrator=sciencedirect_contenthosting
- A deep proteome and transcriptome abundance atlas of 29 healthy human tissues, https://www.embopress.org/doi/full/10.15252/msb.20188503
- A Quantitative Proteome Map of the Human Body, https://www.sciencedirect.com/science/article/pii/S0092867420310783?via%3Dihub#bib57 “We computed the correlation between the protein and RNA abundance across 32 tissues for each gene and found the median Spearman correlation is 0.46 (interquartile range of 0.24–0.65), consistent with previous findings “
- An objection: “Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles”, https://www.sciencedirect.com/science/article/pii/S2667237522001709?via%3Dihub “more reproducibly measured proteins have higher mRNA-protein correlation, suggesting that measurement error limits mRNA-protein correlation”
- If you are looking for protein-level (translation potential) tissue/cell-specific databases (apart from the human proteome atlas), these guys help you to visualize the selected gene/protein level at various protein and transcriptomics level databases: https://generanger.maayanlab.cloud/gene/TEK?database=GTEx_proteomics, https://academic.oup.com/nar/article/51/W1/W213/7160193
- SPIDER: constructing cell-type-specific protein–protein interaction networks, https://academic.oup.com/bioinformaticsadvances/article/4/1/vbae130/7746021
How to correlate/interpret protein-mRNA levels?
- Protein mass changes with the cell size, tissue, and condition
- mRNA and protein turnover rates are also important
- The histone levels are sometimes used to have a better comparison of the profiles because histone copy number is independent of cell size (proportional to DNA amount rather than the volume)
Condition or disease specificity to be discussed as well
- Signaling plasticity in the integrated stress response, https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2023.1271141/full#B80
Expression Atlas, some proteomics comprehensive datasets
- A tissue-specific atlas of mouse protein phosphorylation and expression, https://www.ebi.ac.uk/gxa/experiments/E-PROT-13/Results, https://europepmc.org/article/MED/21183079 i) Half of all sites are unique to a single tissue, and few sites are globally phosphorylated. ii) Classes of sites are differentially enriched in different tissues. iii) Distinct kinases and phosphatases are present in each tissue. iv) Both individual proteins and entire protein complexes show tissue-specific phosphorylation. Phosphorylated and non-phosphorylated proteins display markedly different expression patterns. Phosphoproteins are more often expressed globally, suggesting that tissue-specific phosphorylation allows tuning of ubiquitous proteins to optimize cell performance. Together, complementary protein expression and phosphorylation maintain the unique properties of distinct tissues.
- Human immunochemistry data on 83 different normal cell types from 44 tissue types from the Human Protein Atlas project, https://www.ebi.ac.uk/gxa/experiments/E-PROT-3/Results, https://www.nature.com/articles/nature13302 and https://www.science.org/doi/10.1126/science.1260419
- Individual variability of protein expression in human tissues, https://www.ebi.ac.uk/gxa/experiments/E-PROT-43/Results, https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00580 (only 9 tissues)
