Glucose induced-AKT/mTOR activation accelerates glycolysis and promotes cell survival in acute myeloid leukemia

A predictive metabolomic model for FLT3 and NPM1 mutations in Acute Myeloid Leukemia patients

Cytogenetic abnormalities and gene mutations are essential for planning AML treatment. However, in Turkey, test results typically take 14-30 days. This delay emphasizes a critical need for rapid methods to deliver clinical data in urgent cases requiring immediate treatment decisions. To address this need, our objective was to develop a quick prediction method for NPM1 (Nucleophosmin-1) and FLT3 (FMS-like tyrosine kinase 3) mutations using LC-MS/MS (Liquid Chromatography-Tandem Mass Spectrometry) targeted metabolomics to detect these common and clinically important mutations in de novo AML patients (n = 42) through patient groups and a healthy group. We analyzed metabolic patterns using LC-MS/MS measurements of amino acids and acyl carnitines, key components critical to AML prognosis. The data were then subjected to multivariate analysis techniques. Principal Component Analysis (PCA) revealed that the model explained 79 % of the total variance among the sample groups. To further enhance class discrimination, we conducted Partial Least Squares-Discriminant Analysis (PLS-DA), resulting in R2Y and Q2 values of 0.845 and 0.619, respectively. Using the PLS-DA model, VIP (Variable Importance Projection) identified key metabolites with scores > 1.5, including C0 carnitine, glutamic acid, aspartic acid, tryptophan, histidine, isoleucine, and alpha-aminobutyric acid, respectively, highlighting their potential significance in distinguishing mutation groups. To ensure the validity of the PLS-DA model and evaluate potential overestimation, we validated the model using cross-validation and permutation test, demonstrating its robustness and reliability. Our preliminary model, developed through a targeted metabolomics approach, shows strong fit and predictive capability in determining the mutation status of NPM1 and FLT3 in AML patients.

Lactate dehydrogenase A-coupled NAD+ regeneration is critical for acute myeloid leukemia cell survival

BACKGROUND

Enhanced glycolysis plays a pivotal role in fueling the aberrant proliferation, survival and therapy resistance of acute myeloid leukemia (AML) cells. Here, we aimed to elucidate the extent of glycolysis dependence in AML by focusing on the role of lactate dehydrogenase A (LDHA), a key glycolytic enzyme converting pyruvate to lactate coupled with the recycling of NAD+.

METHODS

We compared the glycolytic activity of primary AML patient samples to protein levels of metabolic enzymes involved in central carbon metabolism including glycolysis, glutaminolysis and the tricarboxylic acid cycle. To evaluate the therapeutic potential of targeting glycolysis in AML, we treated AML primary patient samples and cell lines with pharmacological inhibitors of LDHA and monitored cell viability. Glycolytic activity and mitochondrial oxygen consumption were analyzed in AML patient samples and cell lines post-LDHA inhibition. Perturbations in global metabolite levels and redox balance upon LDHA inhibition in AML cells were determined by mass spectrometry, and ROS levels were measured by flow cytometry.

RESULTS

Among metabolic enzymes, we found that LDHA protein levels had the strongest positive correlation with glycolysis in AML patient cells. Blocking LDHA activity resulted in a strong growth inhibition and cell death induction in AML cell lines and primary patient samples, while healthy hematopoietic stem and progenitor cells remained unaffected. Investigation of the underlying mechanisms showed that LDHA inhibition reduces glycolytic activity, lowers levels of glycolytic intermediates, decreases the cellular NAD+ pool, boosts OXPHOS activity and increases ROS levels. This increase in ROS levels was however not linked to the observed AML cell death. Instead, we found that LDHA is essential to maintain a correct NAD+/NADH ratio in AML cells. Continuous intracellular NAD+ supplementation via overexpression of water-forming NADH oxidase from Lactobacillus brevis in AML cells effectively increased viable cell counts and prevented cell death upon LDHA inhibition.

CONCLUSIONS

Collectively, our results demonstrate that AML cells critically depend on LDHA to maintain an adequate NAD+/NADH balance in support of their abnormal glycolytic activity and biosynthetic demands, which cannot be compensated for by other cellular NAD+ recycling systems. These findings also highlight LDHA inhibition as a promising metabolic strategy to eradicate leukemic cells.

Glycolysis-Driven Prognostic Model for Acute Myeloid Leukemia: Insights into the Immune Landscape and Drug Sensitivity

Background: Acute myeloid leukemia (AML), a malignant blood disease, is caused by the excessive growth of undifferentiated myeloid cells, which disrupt normal hematopoiesis and may invade several organs. Given the high heterogeneity in prognosis, identifying stable prognostic biomarkers is crucial for improved risk stratification and personalized treatment strategies. Although glycolysis has been extensively studied in cancer, its prognostic significance in AML remains unclear. Methods: Glycolysis-related prognostic genes were identified by differential expression profiles. We modeled prognostic risk by least absolute shrinkage and selection operator (LASSO) regression and validated it by Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, and independent datasets (BeatAML2.0, GSE37642, GSE71014). Mechanisms were further explored through immune microenvironment analysis and drug sensitivity scores. Results: Differential expression and survival correlation analysis across the genes associated with glycolysis revealed multiple glycolytic genes associated with the outcomes of AML. We constructed a seven-gene prognostic model (G6PD, TFF3, GALM, SOD1, NT5E, CTH, FUT8). Kaplan-Meier analysis demonstrated significantly reduced survival in high-risk patients (hazard ratio (HR) = 3.4, p < 0.01). The model predicted the 1-, 3-, and 5-year survival outcomes, achieving area under the curve (AUC) values greater than 0.8. Immune profiling indicated distinct cellular compositions between risk groups: high-risk patients exhibited elevated monocytes and neutrophils but reduced Th1 cell infiltration. Drug sensitivity analysis showed that high-risk patients exhibited resistance to crizotinib and lapatinib but were more sensitive to motesanib. Conclusions: We established a novel glycolysis-related gene signature for AML prognosis, enabling effective risk classification. Combined with immune microenvironment analysis and drug sensitivity analysis, we screened metabolic characteristics and identified an immune signature to provide deeper insight into AML. Our findings may assist in identifying new therapeutic targets and more effective personalized treatment regimes.

Identification of metabolite-disease associations based on knowledge graph

BACKGROUND

Despite the insights that metabolite analysis can provide into the onset, development, and progression of diseases-thus offering new concepts and methodologies for prevention, diagnosis, and treatment-traditional wet lab experiments are often time-consuming and labor-intensive. Consequently, this study aimed to develop a machine learning model named COM-RAN, which is based on a knowledge graph and random forest algorithm, to identify potential associations between metabolites and diseases.

METHODS

Firstly, we integrated the known associations between diseases and metabolites. Secondly, we provided a synthesis of the extant data regarding diseases and metabolites, accompanied by supplementary information pertinent to these entities. Thirdly, knowledge graph-based embedded features were used to characterize disease-metabolite associations. Finally, a random forest algorithm was employed to construct a model for identifying potential disease-metabolite associations.

RESULTS

The experimental results demonstrated that the proposed model achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.968 in 5-fold cross-validations, while the Area Under the Precision-Recall Curve (AUPR) was 0.901, outperforming the vast majority of existing prediction methods. The case studies corroborated the majority of the novel associations identified by COM-RAN, thereby further demonstrating the reliability of the current method in predicting the potential relationship between metabolites and diseases.

CONCLUSION

The COM-RAN model demonstrated promise in predicting associations between diseases and metabolites, suggesting that integrating knowledge graphs with machine learning methodologies can significantly improve the accuracy and reliability of predictions related to disease-associated metabolites.

Growth differentiation factor 15 is a glucose-downregulated gene acting as the cross talk between stroma and cancer cells of the human bladder

Hyperglycemia and hyperglycosuria, two primary characteristics of diabetes mellitus, may increase the risk of cancer initiation, particularly for bladder cancer. The effectiveness of metformin, a common antidiabetic agent, is determined by its ability to induce growth differentiation factor 15 (GDF15). However, the mechanism of the GDF15 in relation to glucose, which influences the tumor microenvironment in the human bladder, is not fully understood. This study explores the potential roles of GDF15 in response to glucose in the human bladder. High glucose treatment (30 mM) enhanced phosphorylation of AKT at S473 and AMP-activated protein kinase α1/2 (AMPKα1/2) at S485 to block the counteracting effect of metformin on the AMPK activity in bladder cancer and stroma [human bladder stromal fibroblast (HBdSF) and human bladder smooth muscle cell (HBdSMC)] cells compared with normal glucose treatment (5 mM). Metformin modulated the expressions of GDF15, NDRG1, Maspin, and epithelial-to-mesenchymal transition (EMT) markers to attenuate cell proliferation and invasion of bladder cancer cells. Caffeic acid phenethyl ester (CAPE), like metformin, behaves as an inducer of AMPK activity to stimulate GDF15 expression. Knockdown of GDF15 blocked the downregulation of CAPE on the contraction of HBdSMCs. Both CAPE-induced GDF15 expression and the supernatant from bladder cancer cells with overexpressing GDF15 impeded the HBdSF and HBdSMC migration, suggesting that CAPE-upregulated GDF15 blocked the cell migration. These findings reveal that high glucose treatment inhibits the counteracting effects of either metformin or CAPE on the AMPK activity and GDF15 is downregulated by glucose and induced by metformin and CAPE in both stroma and cancer cells. Furthermore, GDF15 is an antitumor gene facilitating communication between stroma and cancer cells in the human bladder.NEW & NOTEWORTHY This study investigates the counteraction of either CAPE or metformin with the AMPK activity increasing GDF15 expression in human bladder cells. The findings are the first study to indicate the secretion of GDF15 from cancer and stroma cells via autocrine or paracrine mechanisms. Our study suggests that GDF15, an antitumor gene in the human bladder induced by AMPK inducers, acts as a communication link between stroma and cancer cells in the human bladder.

Glycolysis-mTORC1 crosstalk drives proliferation of patient-derived endometrial cancer spheroid cells with ALDH activity

Cancer stem cells are associated with aggressive phenotypes of malignant tumors. A prominent feature of uterine endometrial cancer is the activation of the PI3K-Akt-mTOR pathway. In this study, we present variations in sensitivities to a PI3K-Akt-mTORC1 inhibitor among in vitro endometrial cancer stem cell-enriched spheroid cells from clinical specimens. The in vitro sensitivity was consistent with the effects observed in in vivo spheroid-derived xenograft tumor models. Our findings revealed a complementary suppressive effect on endometrial cancer spheroid cell growth with the combined use of aldehyde dehydrogenase (ALDH) and PI3K-Akt inhibitors. In the PI3K-Akt-mTORC1 signaling cascade, the influence of ALDH on mTORC1 was partially channeled through retinoic acid-induced lactate dehydrogenase A (LDHA) activation. LDHA inhibition was found to reduce endometrial cancer cell growth, aligning with the effects of mTORC1 inhibition. Building upon our previous findings highlighting ALDH-driven glycolysis through GLUT1 in uterine endometrial cancer spheroid cells, curbing mTORC1 enhanced glucose transport via GLUT1 activation. Notably, elevated LDHA expression correlated with adverse clinical survival and escalated tumor grade, especially in advanced stages. Collectively, our findings emphasize the pivotal role of ALDH-LDHA-mTORC1 cascade in the proliferation of endometrial cancer. Targeting the interaction between mTORC1 and ALDH-influenced glycolysis holds promise for developing novel strategies to combat this aggressive cancer.

Metabolic dependencies of acute myeloid leukemia stem cells

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy primarily driven by an immature population of AML cells termed leukemia stem cells (LSCs) that are implicated in AML development, chemoresistance, and relapse. An emerging area of research in AML focuses on identifying and targeting the aberrant metabolism in LSCs. Dysregulated metabolism is involved in sustaining functional properties of LSCs, impeding myeloid differentiation, and evading programmed cell death, both in the process of leukemogenesis and in response to chemotherapy. This review discusses recent discoveries regarding the aberrant metabolic processes of AML LSCs that have begun to change the therapeutic landscape of AML.

LRRC1 knockdown downregulates MACF1 to inhibit the malignant progression of acute myeloid leukemia by inactivating β-catenin/c-Myc signaling

Acute myeloid leukemia (AML) is a hematologic disease associated with genetic abnormalities. This study aimed to explore the role of leucine-rich repeat-containing protein 1 (LRRC1) in the malignant activities of AML and to reveal the molecular mechanism related to microtubule actin cross-linking factor 1 (MACF1). GEPIA database was used to analyze the expression of LRRC1 in bone marrow tissues of AML patients and the correlation between LRRC1 expression and survival analysis. LRRC1 was knocked down to assess the change of AML cell proliferation, cell cycle and apoptosis using CCK-8 assay and flow cytometry. Besides, the contents of extracellular acidification and oxygen consumption rates were measured to evaluate the glycolysis. Additionally, the interaction between LRRC1 and MACF1 predicted by MEM database and was verified by co-immunoprecipitation (Co-IP) assay. Then, MACF1 was overexpressed to conduct the rescue experiments. Expression of proteins in β-catenin/c-Myc signaling was detected by western blot. Finally, AML xenograft mouse model was established to observe the impacts of LRRC1 silencing on the tumor development. Notably upregulated LRRC1 expression was observed in bone marrow tissues of AML patients and AML cells, and patients with the higher LRRC1 expression displayed the lower overall survival. LRRC1 depletion promoted cell cycle arrest and apoptosis and inhibited the glycolysis. Co-IP confirmed the interaction between LRRC1 and MACF1. MACF1 upregulation relieved the impacts of LRRC1 knockdown on the malignant activities of AML cells. Moreover, LRRC1 silencing inhibited the development of xenograft tumor growth of HL-60 cells in nude mice, suppressed MACF1 expression and inactivated the β-catenin/c-Myc signaling. Collectively, LRRC1 knockdown suppressed proliferation, glycolysis and promoted apoptosis in AML cells by downregulating MACF1 expression to inactivate β-catenin/c-Myc signaling.

Crosstalk between autophagy and metabolism: implications for cell survival in acute myeloid leukemia

Acute myeloid leukemia (AML), a prevalent form of leukemia in adults, is often characterized by low response rates to chemotherapy, high recurrence rates, and unfavorable prognosis. A critical barrier in managing refractory or recurrent AML is the resistance to chemotherapy. Increasing evidence indicates that tumor cell metabolism plays a crucial role in AML progression, survival, metastasis, and treatment resistance. Autophagy, an essential regulator of cellular energy metabolism, is increasingly recognized for its role in the metabolic reprogramming of AML. Autophagy sustains leukemia cells during chemotherapy by not only providing energy but also facilitating rapid proliferation through the supply of essential components such as amino acids and nucleotides. Conversely, the metabolic state of AML cells can influence the activity of autophagy. Their mutual coordination helps maintain intrinsic cellular homeostasis, which is a significant contributor to chemotherapy resistance in leukemia cells. This review explores the recent advancements in understanding the interaction between autophagy and metabolism in AML cells, emphasizing their roles in cell survival and drug resistance. A comprehensive understanding of the interplay between autophagy and leukemia cell metabolism can shed light on leukemia cell survival strategies, particularly under adverse conditions such as chemotherapy. This insight may also pave the way for innovative targeted treatment strategies.

The Potential Transcriptomic and Metabolomic Mechanisms of ATO and ATRA in Treatment of FLT3-ITD Acute Myeloid Leukemia

BACKGROUND

Acute myeloid leukemia (AML) with Fms-like tyrosine kinase 3 gene internal tandem duplication (FLT3-ITD) mutations has a poor prognosis. The combination of arsenic trioxide (ATO) and all-trans retinoic acid (ATRA) has a synergistic killing effect on leukemia cells with FLT3-ITD mutation. However, the mechanism, especially the changes of gene expression and metabolic activity remain unclear. Here we explore the transcriptome and metabolomics changes of FLT3-ITD AML cells treated with ATO/ATRA.

METHODS

RNA-seq was used to identify differential expressed genes (DEGs), and ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap mass spectrometry (UHPLC-QE-MS) nontargeted metabolomics method was used to screen out the differential metabolites in FLT3-ITD mutant cell lines treated with ATRA and ATO. KEGG pathway database was utilized for pathway exploration and Seahorse XF24 was used to detect extracellular acidification rate (ECAR). Metabolic polymerase chain reaction (PCR) array and real-time quantitative PCR (RT-qPCR) were used to detect mRNA levels of key metabolic genes of glycolysis and fatty acid after drug treatment.

RESULTS

A total of 3873 DEGs were identified and enriched in 281 Gene Ontology (GO) terms, among which 210 were related to biological processes, 43 were related to cellular components, and 28 were related to molecular functions. Besides, 1794 and 927 differential metabolites were screened in positive and negative ion mode separately, and 59 different metabolic pathways were involved, including alanine-aspartate-glutamate metabolic pathway, arginine, and proline metabolic pathway, glycerophospholipid metabolic pathways, etc. According to KEGG Pathway analysis of transcriptome combined with metabolome, glycolysis/gluconeogenesis pathway and fatty acid metabolism pathway were significantly founded enriched. ATRA + ATO may inhibit the glycolysis of FLT3-ITD AML cells by inhibiting FLT3 and its downstream AKT/HK2-VDAC1 signaling pathway.

CONCLUSIONS

The gene transcription profile and metabolites of FLT3-ITD mutant cells changes significantly after treatment, which might be related to the anti-FLT3-ITD AML effect. The screened DEGs, differential metabolites pathway are helpful in studying the mechanism of anti-leukemia effects and drug targets.