Targeted and Untargeted Approaches in Metabolic Flow Analysis

What is Targeted Metabolic Flow Analysis?

Targeted Metabolic Flow Analysis, based on labeling with isotopes such as 13C or 15N, enables systematic quantification of the flow distribution within specific metabolic pathways in the cellular or tissue networks. This approach allows for the determination of the relative contributions of different metabolic pathways and the distribution of fluxes. Its key advantage lies in the utilization of mass isotopomer information from intracellular metabolites, providing a method that not only visually illustrates the overall trends of metabolic fluxes in many cases but also accurately quantifies the activity of individual metabolic reactions.

Through computational analysis, this method can precisely reveal the activity of various intracellular metabolic reactions, offering in-depth insights into complex cellular processes such as parallel reactions and reversible reactions. It visually unveils the primary active pathways within the cell, along with the relative contributions and distribution changes of each pathway. Consequently, it identifies early diagnostic markers for the occurrence and development of related diseases. Moreover, it elucidates key metabolic pathways and their mutual regulatory patterns, providing a robust scientific basis for the early clinical diagnosis of diseases, targeted drug treatments, and prognosis assessments.

Application of Targeted Metabolic Flow Analysis

Targeted metabolic flow analysis finds application in various directions, providing valuable insights into different environmental conditions and metabolic pathways associated with various metabolic diseases. By comparing the distribution changes in metabolic fluxes under diverse environmental conditions and within different metabolic pathways implicated in metabolic diseases, this analytical approach unveils the major metabolic pathways involved in the occurrence and development of related diseases. Additionally, it identifies early diagnostic markers crucial for understanding the progression of these diseases.

The utilization of 13C metabolic flux techniques to trace changes in intracellular and extracellular intermediate metabolites enables the identification of critical metabolic pathways and activities in genetically engineered organisms. This, in turn, serves as direct evidence for enhancing the synthesis of target metabolic products. The application of this method allows for the precise determination of key metabolic routes, offering a foundation for optimizing the production of desired metabolic compounds.

Furthermore, targeted metabolic flow analysis facilitates the comparative analysis of cellular, tissue, blood, and urine samples, examining changes in metabolic functions before and after genetic modifications. This comprehensive analysis provides a detailed understanding of the alterations in metabolic functionality associated with genetic engineering, offering valuable insights into the impact of genetic modifications on cellular and metabolic processes.

Case of Targeted Metabolic Flow Analysis

Metabolic reprogramming of tumor cells

Tumor cells undergo metabolic reprogramming characterized by heightened energy and synthetic metabolic demands, essential for their survival and proliferation under nutrient-deprived conditions. According to this study, the loss of PKCζ promotes metabolic reprogramming in cancer cells, allowing them to adapt to glucose deficiency by modulating the serine synthesis pathway to utilize an alternative nutrient, glutamine. PKCζ acts to inhibit the expression of two crucial enzymes, PHGDH and PSAT1, within this pathway. Additionally, it inhibits the enzymatic activity of PHGDH through phosphorylation. Researchers observed that the absence of PKCζ in mice led to an increased incidence of intestinal tumors, accompanied by elevated expression of these two metabolic enzymes. Conversely, patients with low levels of PKCζ exhibited a poorer prognosis. Furthermore, in human colorectal tumors, the activity of PKCζ and caspase-3 was found to be associated with PHGDH. These findings unveil the critical role of the tumor metabolic enzyme inhibitor, PKCζ, in human cancer.

Figure 1. Metabolic reprogramming of PKCζ via glutamine (Ma et al., 2013)

Untargeted Metabolic Flow Analysis

Classical metabolomics can reflect changes in the body's metabolites and potential pathways that may be activated. However, a single metabolite may participate in multiple metabolic pathways without undergoing changes in abundance. Untargeted metabolic flux analysis allows for the study of dynamic changes in metabolic flux over time, unconstrained by specific metabolic pathways. It involves quantitative analysis of the flux distribution through metabolic pathways, providing a comprehensive understanding of how metabolites change within biological pathways. This approach elevates the research of metabolomics to a higher level and a more intricate understanding.

Metabolic flux analysis, especially techniques based on 13C labeling, has emerged as a focal point in recent research. These methodologies enable the dynamic exploration of metabolic flow patterns, offering insights into the quantitative analysis of fluxes through various metabolic pathways. By going beyond the constraints of specific pathways, untargeted metabolic flux analysis enhances our ability to interpret how metabolites change within the intricate network of biological pathways.

Application of Untargeted Metabolic Flow Analysis

Studying the dynamic changes in cellular metabolism and the precise distribution of metabolic pathway fluxes is essential for gaining insights into the intricate processes governing cell metabolism. By comparing the distribution changes in metabolic fluxes across different pathways, researchers can unravel the major metabolic routes implicated in the occurrence and progression of relevant diseases. This comparative analysis contributes to an enhanced understanding of the pathological mechanisms underlying these diseases.

Tracking the changes in intracellular and extracellular metabolites allows for the identification of key pathways and genes associated with signal transduction pathways like cell proliferation. This approach provides valuable information on critical pathways involved in cellular growth and other signaling processes. Overall, the comprehensive investigation of cellular metabolism, encompassing dynamic changes and flux distribution within metabolic pathways, not only advances our understanding of physiological processes but also holds great potential for uncovering key insights into disease pathogenesis and signaling pathway regulation.

Case of Untargeted Metabolic Flow Analysis

In this study, an innovative untargeted isotope metabolism analysis method, termed untargeted metabolic flux, was proposed and implemented using isotope labeling technology. This approach involves the application of untargeted metabolomics to detect changes in the abundance of isotopically labeled substances, thereby facilitating the analysis of the distribution of metabolic pathway fluxes. The study also provides a comprehensive overview of the analytical workflow for untargeted isotope-labeled metabolic flux.

To illustrate the application of untargeted metabolic flux analysis, the metabolic changes in human lung cancer cells under hypoxic conditions were investigated. By monitoring changes in metabolic flux, it was observed that the increase in glutamine promoted the production of acetyl-CoA, leading to the synthesis of N-acetyl aspartic acid (NAA) catalyzed by NAT8L in A549 lung adenocarcinoma cells. Silencing NAT8L was found to inhibit the proliferation of A549, JHH-4, PH5CH8, and BEAS-2B cells. Additionally, the study highlighted the potential significant role of NAA in cancer cell metabolism.

Untargeted metabolic flux analysis addresses a significant bottleneck in current metabolomics research, elevating the study of the metabolome to a higher level. This methodological advancement provides a more comprehensive understanding of metabolic pathways and their flux distributions, offering valuable insights into the intricacies of cellular metabolism, particularly in the context of cancer cell responses to environmental conditions such as hypoxia.

Figure 2. Workflow for non-targeted mass isotopolome analysis.

Figure 3. Effects of different oxygen levels on MIDs and metabolic fluxes in lung cancer cells fed with [1,2-13C2]glucose or [U-13C]glutamine.

References

1. Ma, Li, et al. "Control of nutrient stress-induced metabolic reprogramming by PKCζ in tumorigenesis." Cell 152.3 (2013): 599-611.
2. Weindl, Daniel, et al. "Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis." Cancer & metabolism 4 (2016): 1-14.

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