Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment

Discovering the strength of immunometabolism in cancer therapy: Employing metabolic pathways to enhance immune responses

Immunometabolism, which studies cellular metabolism and immune cell function, is a possible cancer treatment. Metabolic pathways regulate immune cell activation, differentiation, and effector functions, crucial to tumor identification and elimination. Immune evasion and tumor growth can result from tumor microenvironment metabolic dysregulation. These metabolic pathways can boost antitumor immunity. This overview discusses immune cell metabolism, including glycolysis, oxidative phosphorylation, amino acid, and lipid metabolism. Amino acid and lipid metabolic manipulations may improve immune cell activity and antitumor immunity. Combination therapy using immunometabolism-based strategies may enhance therapeutic efficacy. The complexity of the metabolic network, biomarker development, challenges, and future approaches are all covered, along with a summary of case studies demonstrating the effectiveness of immunometabolism-based therapy. Metabolomics, stable isotope tracing, single-cell analysis, and computational modeling are also reviewed for immunometabolism research. Personalized and combination treatments are considered. This review adds to immunometabolism expertise and sheds light on metabolic treatments' ability to boost cancer treatment immunological response. Also, in this review, we discussed the immune response in cancer treatment and altering metabolic pathways to increase the immune response against malignancies.

Flux sampling in genome-scale metabolic modeling of microbial communities

BACKGROUND

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates.

RESULTS

In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux.

CONCLUSIONS

Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.

Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer

Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.

Involvement of ACACA (acetyl-CoA carboxylase α) in the lung pre-metastatic niche formation in breast cancer by senescence phenotypic conversion in fibroblasts

BACKGROUND

Reprogramming of metabolism is strongly associated with the development of cancer. However, the role of metabolic reprogramming in the remodeling of pre-metastatic niche (PMN), a key step in metastasis, is still unknown. We aimed to investigate the metabolic alternation during lung PMN formation in breast cancer.

METHODS

We assessed the transcriptomes and lipidomics of lung of MMTV-PyVT mice by microarray and liquid chromatography-tandem mass mass spectrometry before lung metastasis. The validation of gene or protein expressions was performed by quantitative real-time polymerase chain reaction or immunoblot and immunohistochemistry respectively. The lung fibroblasts were isolated from mice and then co-cultured with breast cancer to identify the influence of cancer on the change of lung fibroblasts in PMN.

RESULTS

We demonstrated changes in the lipid profile and several lipid metabolism genes in the lungs of breast cancer-bearing MMTV-PyVT mice before cancer spreading. The expression of ACACA (acetyl-CoA carboxylase α) was downregulated in the lung fibroblasts, which contributed to changes in acetylation of protein's lysine residues and the synthesis of fatty acid. The downregulation of ACACA in lung fibroblasts triggered a senescent and inflammatory phenotypic shift of lung fibroblasts in both in vivo and in vitro models. The senescence-associated secretory phenotype of lung fibroblasts enabled the recruitment of immunosuppressive granulocytic myeloid-derived suppressor cells into the lungs through the production of CXCL1 in the lungs. Knock-in of ACACA prevented lung metastasis in the MMTV-PyVT mouse model, further supporting that ACACA was involved in the remodeling of the lung PMN.

CONCLUSIONS

Taken together, these data revealed a mechanism by which ACACA downregulation directed the formation of an immunosuppressive lung PMN in breast cancer.

Identification of a lipid metabolism-related gene for cancer immunotherapy

Background: Tumors frequently evade immune surveillance through multiple pathways to escape T cell recognition and destruction. Previous studies indicated that lipid metabolism alteration could affect the anti-tumor immunity of cancer cells. Nonetheless, the studies that investigated lipid metabolism-related gene for cancer immunotherapy are still few. Materials and methods: By mining the TCGA database, we screened out carnitine palmitoyltransferase-2 (CPT2), a key enzyme in the fatty acid β-oxidation (FAO) process associated with anti-tumor immunity. We then analyzed the gene expression and clinicopathological features of CPT2 using open-source platforms and databases. Molecular proteins interacting with CPT2 were also identified using web interaction tools. Subsequently, the relationship between CPT2 and survival was analyzed in cancer patients. Results: Our study revealed that CPT2 played a vital role in tumor microenvironment and immune response signaling pathways. We have also demonstrated that increased CPT2 gene expression could enhance the level of tumor immune cell infiltration. Furthermore, high CPT2 expression positively related with overall survival associated with immunotherapy. CPT2 expression was also associated with the prognosis of human cancers, suggesting that CPT2 may be a potential biomarker for predicting the efficacy of cancer immunotherapy. Conclusion: To the best of our knowledge, the relationship between CPT2 and tumor immune microenvironment was first proposed in this study. Therefore, further studies on CPT2 may provide new insights into the development of effective cancer immunotherapy.

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.

Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer

1Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.

Systems engineering of Escherichia coli for high-level shikimate production

To further increase the production efficiency of microbial shikimate, a valuable compound widely used in the pharmaceutical and chemical industries, ten key target genes contributing to shikimate production were identified by exploiting the enzyme constraint model ec_iML1515, and subsequently used for promoting metabolic flux towards shikimate biosynthesis in the tryptophan-overproducing strain Escherichia coli TRP0. The engineered E. coli SA05 produced 78.4 g/L shikimate via fed-batch fermentation. Deletion of quinate dehydrogenase and introduction of the hydroaromatic equilibration-alleviating shikimate dehydrogenase mutant AroE^T61W/L241I reduced the contents of byproducts quinate (7.5 g/L) and 3-dehydroshikimic acid (21.4 g/L) by 89.1% and 52.1%, respectively. Furthermore, a high concentration shikimate responsive promoter P_rpoS was recruited to dynamically regulate the expression of the tolerance target ProV to enhance shikimate productivity by 23.2% (to 2 g/L/h). Finally, the shikimate titer was increased to 126.4 g/L, with a yield of 0.50 g/g glucose and productivity of 2.63 g/L/h, using a 30-L fermenter and the engineered strain E. coli SA09. This is, to the best of our knowledge, the highest reported shikimate titer and productivity in E. coli.

Interaction between glycolysis‒cholesterol synthesis axis and tumor microenvironment reveal that gamma-glutamyl hydrolase suppresses glycolysis in colon cancer

Background

Metabolic reprogramming is a feature of cancer. However, colon cancer subtypes based on the glycolysis‒cholesterol synthesis axis have not been identified, and little is known about connections between metabolic features and the tumor microenvironment.

Methods

Data for 430 colon cancer cases were extracted from The Cancer Genome Atlas, including transcriptome data, clinical information, and survival outcomes. Glycolysis and cholesterol synthesis-related gene sets were obtained from the Molecular Signatures Database for a gene set variation analysis. The relationship between the genomic landscape and immune landscape were investigated among four metabolic subtypes. Hub genes were determined. The clinical significance of candidate hub gene was evaluated in 264 clinical samples and potential functions were validated in vitro and in vivo.

Results

Colon cancer cases were clustered into four metabolic subtypes: quiescent, glycolytic, cholesterogenic, and mixed. The metabolic subtypes differed with respect to the immune score, stromal score, and estimate score using the ESTIMATE algorithm, cancer-immunity cycle, immunomodulator signatures, and signatures of immunotherapy responses. Patients in the cholesterogenic group had better survival outcomes than those for other subtypes, especially glycolytic. The glycolytic subtype was related to unfavorable clinical characteristics, including high mutation rates in TTN, APC, and TP53, high mutation burden, vascular invasion, right colon cancer, and low-frequency microsatellite instability. GGH, CACNG4, MME, SLC30A2, CKMT2, SYN3, and SLC22A31 were identified as differentially expressed both in glycolytic-cholesterogenic subgroups as well as between colon cancers and healthy samples, and were involved in glycolysis‒cholesterol synthesis. GGH was upregulated in colon cancer; its high expression was correlated with CD4⁺ T cell infiltration and longer overall survival and it was identified as a favorable independent prognostic factor. The overexpression of GGH in colon cancer-derived cell lines (SW48 and SW480) inhibited PKM, GLUT1, and LDHA expression and decreased the extracellular lactate content and intracellular ATP level. The opposite effects were obtained by GGH silencing. The phenotype associated with GGH was also validated in a xenograft nude mouse model.

Conclusions

Our results provide insight into the connection between metabolism and the tumor microenvironment in colon cancer and provides preliminary evidence for the role of GGH, providing a basis for subsequent studies.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Genome-Scale Metabolic Model Analysis of Metabolic Differences between Lauren Diffuse and Intestinal Subtypes in Gastric Cancer

Gastric cancer (GC) is one of the most lethal cancers worldwide; it has a high mortality rate, particularly in East Asia. Recently, genetic events (e.g., mutations and copy number alterations) and molecular signaling associated with histologically different GC subtypes (diffuse and intestinal) have been elucidated. However, metabolic differences among the histological GC subtypes have not been studied systematically. In this study, we utilized transcriptome-based genome-scale metabolic models (GEMs) to identify differential metabolic pathways between Lauren diffuse and intestinal subtypes. We found that diverse metabolic pathways, including cholesterol homeostasis, xenobiotic metabolism, fatty acid metabolism, the MTORC1 pathway, and glycolysis, were dysregulated between the diffuse and intestinal subtypes. Our study provides an overview of the metabolic differences between the two subtypes, possibly leading to an understanding of metabolism in GC heterogeneity.

New Immunometabolic Strategy Based on Cell Type-Specific Metabolic Reprogramming in the Tumor Immune Microenvironment

Immunometabolism is an emerging discipline in cancer immunotherapy. Tumor tissues are heterogeneous and influenced by metabolic reprogramming of the tumor immune microenvironment (TIME). In the TIME, multiple cell types interact, and the tumor and immune cells compete for limited nutrients, resulting in altered anticancer immunity. Therefore, metabolic reprogramming of individual cell types may influence the outcomes of immunotherapy. Understanding the metabolic competition for access to limited nutrients between tumor cells and immune cells could reveal the breadth and complexity of the TIME and aid in developing novel therapeutic approaches for cancer. In this review, we highlight that, when cells compete for nutrients, the prevailing cell type gains certain advantages over other cell types; for instance, if tumor cells prevail against immune cells for nutrients, the former gains immune resistance. Thus, a strategy is needed to selectively suppress such resistant tumor cells. Although challenging, the concept of cell type-specific metabolic pathway inhibition is a potent new strategy in anticancer immunotherapy.