Proteomics, the study of proteins on a large scale, involves the comprehensive identification and quantification of all proteins present in a biological sample. This powerful technique has paved the way for researchers to explore how protein expression levels change in response to various conditions, such as cell cycle progression, disease states, or drug treatments. By employing advanced mass spectrometry-based methods like TMT (Tandem Mass Tagging), scientists can simultaneously quantify thousands of proteins, providing a dynamic snapshot of the proteome at a given moment.
Metabolomics, on the other hand, focuses on the analysis of small molecules known as metabolites. These molecules serve as the building blocks of cellular processes and are instrumental in energy production, signaling, and regulation. Metabolomics allows researchers to profile the complete set of metabolites in a biological sample, offering insights into metabolic pathways, biomarker discovery, and the impact of cellular metabolism on disease progression. Techniques like MRM (Multiple Reaction Monitoring) enable targeted quantification of specific metabolites, enhancing the precision of metabolomic analyses.
The integration of proteomic and metabolomic approaches holds the promise of unraveling intricate biological networks. By examining changes in both protein expression and metabolite levels simultaneously, scientists can gain a more comprehensive understanding of cellular responses to stimuli, disease mechanisms, and the crosstalk between different molecular components. This holistic perspective fosters new avenues of research, leading to discoveries that impact fields ranging from basic biology to clinical applications.
Case. Proteomic and Metabolomic Characterization of a Mammalian Cellular Transition from Quiescence to Proliferation
This study delves into the intricate metabolic changes occurring in FL5.12 cells during the G0/G1 transition phase in response to Interleukin-3 (IL-3). The choice of this model system allows for an investigation of cell cycle transitions without the complexities associated with serum signaling. The research aims to draw parallels with cancer-related studies, highlighting the similarities in metabolic characteristics.
FL5.12 cells are a murine pro-B lymphocyte cell line that has been genetically modified to express Bcl-2, an anti-apoptotic protein. These cells are cultured in RPMI 1640 medium supplemented with calf bovine serum, antibiotics, and IL-3. The addition and withdrawal of IL-3 play a pivotal role in controlling cell proliferation and quiescence, allowing for a controlled experimental setup.
The study employs a multi-omics approach, combining mass spectrometry-based proteomics and targeted mass spectrometry-based metabolomics. This approach enables the comprehensive profiling of protein and metabolite abundance across different phases of the cell cycle, providing insights into the dynamic changes that occur during the G0/G1 transition.
By examining the metabolic adaptations in FL5.12 cells during the cell cycle, this research not only contributes to our understanding of cell cycle regulation but also offers valuable insights into cancer metabolism, potentially identifying metabolic enzyme targets and pathways for therapeutic interventions. The study's findings are expected to have implications for cancer research, particularly in the context of acute myeloid leukemias (AMLs) that exhibit abnormalities in IL-3 receptor expression.
Research Materials
Control Group: Mouse primary B lymphocytes were washed three times with PBS and then cultured in medium without IL-3 for 36 hours.
Experimental Group: Mouse primary B lymphocytes, after being maintained in a quiescent state for 36 hours, were stimulated with IL-3 to induce the transition from quiescence to the cell proliferation cycle. Cells were collected at 0h, 4h, 8h, 12h, 16h, and 20h after the addition of IL-3 (corresponding to the cell proliferation stages of G0, early G1, G1, G1-S, S, and G2/M, respectively) for analysis of protein expression changes and metabolite expression changes. The schematic diagram is as follows:
Technical Methods
Proteomics: The study employed TMT-MS (Tandem Mass Spectrometry) proteomics to quantify protein abundance levels. This technique allowed the measurement of dynamic changes in functional protein modules during the cell cycle.
Metabolomics: Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) was used for targeted metabolomics. This method helped analyze intracellular and extracellular metabolite profiles, revealing dynamic changes in metabolite levels during different cell cycle phases.
Bioinformatic and Statistical Analysis: Data analysis involved quantification of abundance levels of proteomics, metabolomics, and protein modules. Statistical significance was determined with p-values, with values less than 0.05 considered significant.
Data Availability: All data generated in the study, including proteomics, metabolomics, and protein module profiling, are available for further analysis.
Results
Protein Expression Changes at Different Stages of the Cell Proliferation Cycle
TMT proteomic analysis was conducted on cells at different stages of the cell proliferation cycle, quantifying a total of 6,700 proteins. Among these, 2,666 proteins were identified in both biological replicates with good parallelism. Cluster analysis of these proteins revealed a gradual change in overall protein expression as the cell cycle progressed. Subsequently, 60 key proteins with significant differential changes related to the cell cycle progression were selected for trend analysis, indicating their involvement in cell proliferation processes.
Protein Module Analysis at Different Stages of the Cell Proliferation Cycle
In addition to analyzing the expression levels of individual proteins with known annotations, the authors used the COMPLEAT tool and literature searches to identify 311 highly credible Protein Modules (groups of functionally related proteins). Scoring and p-value analysis were performed for each stage's Protein Modules to identify important modules and the biological processes they were involved in. Notably, during the G0 phase, the expression of proteins in four modules related to the TCA cycle was significantly downregulated, resembling the metabolic characteristics of tumor cells with mitochondrial metabolism inhibition and active glycolysis. This prompted further investigation into the expression changes of metabolic enzymes in the TCA pathway, revealing their alignment with metabolic features.
Integrated Analysis of Proteomics and Metabolomics Reveals Patterns of Change During Cell Proliferation
Proteomic changes were pronounced during the transition from G0 to G1, while metabolomic changes were significant during the G1/S transition. The authors aimed to validate whether the expression levels of relevant metabolites were consistent with the expression levels of metabolic enzymes. Eight key metabolic pathways (TCA cycle, glycolysis, de novo pyrimidine biosynthesis, de novo purine biosynthesis, pyrimidine salvage, purine degradation, lipid synthesis, and the urea cycle) were selected for targeted MRM detection of metabolites. The results indicated that in pathways such as TCA, glycolysis, and lipid metabolism, the trends in enzyme expression did not align closely with the expression changes of related metabolites. However, in more refined pathways like nucleotide metabolism, better correlations were observed between enzymes and metabolites.
Further correlation analysis of metabolic enzymes and metabolites in different pathways revealed that some metabolites were likely regulated by enzymes from multiple pathways, leading to dynamic changes. An interesting finding was a positive correlation between enzymes in the TCA cycle and metabolites in the urea cycle, while a negative correlation existed with metabolites from glycolysis and the TCA cycle. Metabolites from glycolysis and the TCA cycle positively correlated with enzymes in pyrimidine/purine synthesis pathways. Enzymes in the glycolytic pathway exhibited higher correlations with enzymes from other pathways than within the glycolytic pathway itself, suggesting that glycolytic metabolites serve as precursors for other pathways such as amino acid and nucleotide synthesis.
Reference
- Lee, Ho-Joon, et al. "Proteomic and metabolomic characterization of a mammalian cellular transition from quiescence to proliferation." Cell reports 20.3 (2017): 721-736.
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