bims-glucam Biomed News
on Glutamine cancer metabolism
Issue of 2026–07–19
thirteen papers selected by
Sreeparna Banerjee, Middle East Technical University
  1. The Interdependence of Carbon Substrates and Metabolic Pathways Enables the Metabolic Plasticity of Cancer Cells.
  2. Integrated multi-omics and machine learning identify OGDHL as a key regulator of glioblastoma progression and antitumor immunity via glutamine metabolism and histone lactylation.
  3. Oncogenic PIK3CA reprograms glutamine metabolism to drive bladder cancer progression.
  4. NRF2/xCT-Mediated Antioxidant Adaptation Attenuates Apoptosis Induced by ASCT2 Inhibition in Prostate Cancer.
  5. KRAS on Empty: Lipid Oxidation Blockade Reveals a Metabolic Achilles' Heel in Pancreatic Cancer.
  6. S100A8+S100A9+ transitional macrophages are associated with pulmonary fibrosis progression by integrating immunometabolism and fibrogenic crosstalk.
  7. Effects of different drugs on radiation-induced oral mucositis in patients with head and neck tumors: a systematic review and a network meta-analysis.
  8. Exploring potential associations between blood metabolites and cirrhosis risk: a Mendelian randomization and LC-MS/MS analysis.
  9. Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.
  10. Serum 1H NMR-Based Untargeted Metabolomics for Noninvasive Early Detection of Breast Cancer.
  11. Branched-Chain and Aromatic Amino Acids Mark Early Metabolic Shifts in Adults with Varying Adiposity.
  12. Pan-Cancer Landscape of Spatial PTM and Mutation Clusters Reveals Allosteric Regulation of Protein-Protein Interactions.
  13. A transcriptional biosensor reveals mechanisms of α-ketoglutarate signaling to chromatin.
  1. Adv Exp Med Biol. 2026 ;1501 1-42
    The Interdependence of Carbon Substrates and Metabolic Pathways Enables the Metabolic Plasticity of Cancer Cells.
    Inês Serpa-Gonçalves, Jacinta Serpa.
      The tumor microenvironment (TME) functions as a dynamic and co-evolving ecosystem, where malignant and non-malignant cells form a metabolically interdependent community. This ecological view reimagines tumors not as isolated cell masses, but as complex biotopes in which cellular interactions are integral to tumor initiation, growth, and progression. A hallmark of this adaptive environment is metabolic plasticity-an essential mechanism that enables tumor cells, including the metastatic ones, to reprogram their metabolism in response to fluctuating nutrient availability and environmental stressors. At the core of this reprogramming lies carbon metabolism, characterized by the selective and flexible utilization of key metabolites, including glucose, lactate, glutamine, cysteine, and fatty acids. These compounds support energy production, biomass synthesis, and redox balance, while also facilitating the export and repurposing of metabolic byproducts for signaling or reuse. This chapter presents a conceptual framework that explores the interdependence of central metabolic pathways, emphasizing how tumors coordinate energy generation, biosynthesis, and redox control to support malignant progression.
    Keywords:  Gluconeogenesis; Glutaminolysis; Glycolysis; Interconnected metabolic pathways; Metabolic dependence; Metabolism-targeted therapies; Pentose phosphate pathway (PPP)
    DOI:  https://doi.org/10.1007/978-3-032-12166-0_1
  2. Int J Neurosci. 2026 Jul 15. 1-22
    Integrated multi-omics and machine learning identify OGDHL as a key regulator of glioblastoma progression and antitumor immunity via glutamine metabolism and histone lactylation.
    Xindong Shi, Xiao Hu, Ziyan Shi, Fang Chen.
       BACKGROUND: Glioblastoma (GBM) has an extremely poor prognosis, and its malignant progression is closely associated with glutamine metabolic reprogramming and immune evasion; however, the key regulatory networks remain unclear.
    METHODS: This study integrated bioinformatics data and identified key proteins through weighted gene co-expression network analysis (WGCNA), screening for differentially expressed proteins (DEPs), and machine learning algorithms. The functions and molecular mechanisms were validated using in vitro cell experiments and in vivo mouse models.
    RESULTS: Oxoglutarate dehydrogenase L (OGDHL) was identified as the key protein in GBM it was down-regulated in both GBM and low-grade glioma (LGG) tissues and was correlated with immune cell infiltration. OGDHL overexpression inhibited GBM cell proliferation and reduced glutamate, α-ketoglutarate (α-KG), and lactate production and programmed death-ligand 1 (PD-L1) expression, while promoting apoptosis. OGDHL overexpression enhanced CD8+ T cell-mediated cytotoxicity and interferon-γ (IFN-γ) secretion. Mechanistically, OGDHL overexpression suppressed histone H3 lysine 18 lactylation (H3K18la) enrichment, reduced luciferase activity, and inhibited PD-L1 expression in GBM cells, effects that were rescued by exogenous lactate supplementation. In vivo, OGDHL up-regulation inhibited tumor growth, reduced glutamate and lactate production, and decreased Ki-67- and PD-L1-positive cells, while increasing OGDHL-positive cells.
    CONCLUSION: OGDHL exerts a tumor-suppressive function in GBM by regulating glutamine metabolism and histone lactylation-mediated PD-L1 expression, representing a potential new target for immunometabolic therapy.
    Keywords:  Glioblastoma; Glutamine metabolism; Histone lactylation; Immune escape; Oxoglutarate dehydrogenase L; Programmed death-ligand 1
    DOI:  https://doi.org/10.1080/00207454.2026.2705220
  3. Cell Commun Signal. 2026 Jul 14.
    Oncogenic PIK3CA reprograms glutamine metabolism to drive bladder cancer progression.
    Karthik Reddy Kami Reddy, Vasanta Putluri, Danthasinghe Waduge Badrajee Piyarathna, Jun Hyoung Park, Aaditya Krishna Arun, Sachin B Jorvekar, Yongchao Dou, Mohammed Khurshidul Hassan, Chandra Sekhar Amara, Abu Hena Mostafa Kamal, Chandra Shekar R Ambati, Jun Xu, Daniel Kraushaar, Adriana Langer Gramer, Allison C Bellman, Leomar Y Ballester, Yuan Xu, Robert Taylor Ripley, Seth P Lerner, Roni J Bollag, Martha K Terris, Chong-Xian Pan, Benny Abraham Kaipparettu, Yair Lotan, Nagireddy Putluri.
       BACKGROUND: Genomic analysis has revealed that approximately 40% of bladder cancer (BLCA) tumors harbor alterations in the PI3K/AKT pathway, with PIK3CA mutations occurring in 15-25% of cases. PIK3CA, which encodes the catalytic p110α subunit of PI3K, plays a critical role in regulating cell survival, proliferation, and metabolism. However, the metabolic and functional consequences of PIK3CA mutations in BLCA remain poorly defined.
    METHODS: To investigate the role of PIK3CA mutations in BLCA, we performed targeted sequencing on tumors from patients, identifying recurrent alterations. Using CRISPR/Cas9 knock-in models in SCaBER and UM-UC-3 cell lines, we introduced the PIK3CA E545K mutation to study its effects. We conducted transcriptomic profiling, targeted metabolomics, and stable isotope tracing to assess metabolic reprogramming. Functional assays measured proliferation, mitochondrial complex I activity, and glutaminolysis. Orthotopic xenografts in mice were used to evaluate in vivo tumor growth and metabolism.
    RESULTS: PIK3CA mutations were present in 20% of cases, consistent with TCGA data. The E545K and E545Q hotspots accounted for 70% of these mutations. PIK3CA E545K strongly activated PI3K/AKT signaling. Transcriptomic analysis revealed enrichment of OXPHOS, fatty acid metabolism, and mTORC1 signaling. Metabolomics indicated changes in TCA cycle metabolites and enhanced reductive carboxylation of glutamine to citrate, driving fatty acid synthesis. Mutant cells showed increased expression of GLS1 and FASN, higher proliferation rates, and elevated mitochondrial complex I activity. In vivo, PIK3CA-mutant xenografts displayed significantly increased tumor growth.
    CONCLUSION: PIK3CA mutations are frequent drivers of metabolic reprogramming in BLCA, leading to increased glutamine flux, elevated OXPHOS activity, and enhanced fatty acid synthesis, all of which contribute to tumor progression. These findings provide the first comprehensive evidence that PIK3CA-driven metabolic alterations are both biomarkers of aggressive disease and actionable therapeutic targets. The efficacy of PI3Kα inhibition in combination with metabolic targets may support its potential in precision medicine for PIK3CA-mutant BLCA and highlights the value of integrating metabolic biomarkers into treatment strategies for advanced BLCA.
    DOI:  https://doi.org/10.1186/s12964-026-03058-w
  4. Cancer Sci. 2026 Jul 16.
    NRF2/xCT-Mediated Antioxidant Adaptation Attenuates Apoptosis Induced by ASCT2 Inhibition in Prostate Cancer.
    Jiaxing Wei, Yasutaka Yamada, Takaaki Tamura, Masahiro Sugiura, Xue Zhao, Meng Ni, Adilan Aierken, Tomokazu Sazuka, Yusuke Imamura, Shinichi Sakamoto.
      Glutamine supports biosynthesis and redox homeostasis in cancer cells. The glutamine transporter ASCT2 is highly expressed in prostate cancer and is associated with higher Gleason grade. Although ASCT2 inhibition induces reactive oxygen species (ROS) accumulation and apoptosis, cancer cells may activate antioxidant adaptive mechanisms that limit therapeutic efficacy. Here, we identified an NRF2/xCT-dependent redox-adaptive response following ASCT2 inhibition in prostate cancer. Mechanistically, ASCT2 inhibition induced ROS accumulation, promoted nuclear translocation of NRF2, and upregulated xCT together with other NRF2-associated antioxidant genes. Functional redox analysis showed that ASCT2 inhibition reduced glutathione-dependent redox capacity, whereas combined ASCT2 and xCT inhibition further increased GSSG accumulation and markedly decreased GSH levels and the GSH/GSSG ratio. These fin\dings were further validated in C4-2 cells as an additional prostate cancer model. Cell death discrimination assays showed that combined ASCT2 and xCT inhibition induced lipid peroxidation that was partially rescued by ferrostatin-1, whereas z-VAD produced a stronger rescue effect and Annexin V/PI staining confirmed prominent apoptotic cell death. In castrated 22Rv1 xenograft models, combined treatment with V-9302 and erastin produced the strongest inhibition of tumor growth without significant body weight loss during treatment. Collectively, these findings demonstrate that NRF2/xCT-mediated residual glutathione redox buffering functions as an adaptive survival mechanism following ASCT2 inhibition and provide a mechanistic rationale for combined targeting of ASCT2 and xCT to overcome redox-adaptive resistance in prostate cancer.
    Keywords:  ASCT2; NRF2; oxidative stress; prostate cancer; xCT
    DOI:  https://doi.org/10.1111/cas.70437
  5. Cancer Res. 2026 Jul 15. 86(14): 3374-3376
    KRAS on Empty: Lipid Oxidation Blockade Reveals a Metabolic Achilles' Heel in Pancreatic Cancer.
    Deborah de la Caridad Delgado Herrera, Christina M Ferrer.
      Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States, driven by its aggressive biology and high metastatic incidence at diagnosis. With a 5-year survival rate of just 8%, PDAC remains one of the most lethal cancers. Mutant KRAS, present in more than 90% of cases, serves as a key driver of tumorigenesis and metabolic reprogramming. In this issue of Cancer Research, Thakur and colleagues uncover a novel metabolic adaptation that PDAC cells use to survive therapeutic stress. Their integrated metabolomic and lipidomic analyses show that ERK inhibition-targeting a key KRAS pathway effector-not only disrupts glycolysis and glutamine metabolism but also triggers a compensatory increase in fatty acid oxidation (FAO). This shift occurs through lipophagy, a lysosome-mediated lipid degradation process, rather than cytosolic lipolysis. Mechanistically, ERK inhibition promotes the nuclear translocation of TFEB, which drives the upregulation of FAO and lipophagy genes. This metabolic reprogramming enables PDAC cells to survive KRAS pathway blockade. Importantly, cotargeting FAO alongside ERK or KRAS inhibitors elicits a potent synergistic antitumor effect in vivo. This dual-target strategy holds promise for overcoming PDAC resistance to KRAS-targeted therapies, laying the groundwork for novel combination treatments. See related article by Thakur et al., p. 3519.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-25-1877
  6. Front Immunol. 2026 ;17 1832940
    S100A8+S100A9+ transitional macrophages are associated with pulmonary fibrosis progression by integrating immunometabolism and fibrogenic crosstalk.
    Censhan Lin, Mingyue Wu, Ziying Zhang, Muxu Zheng, Huiqin Li, Yufang Wang, Zhanxin Du, Linxiang Wu, Junjie Dai, Yu Yu, Xuhang Du, Yehui Wu, Jie Xu, Zhongxing Wang, Fu-Li Xiang.
       Background: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options. Although macrophage heterogeneity has been implicated in pathogenesis, the contribution of transitional macrophage states remains poorly understood.
    Methods: We integrated single-cell RNA-seq from 93 human lung specimens (44 IPF, 49 controls) and analyzed peripheral blood single-cell datasets (31 IPF, 17 controls). We examined differentiation trajectories and ligand-receptor networks and assessed metabolic programs. Functional relevance was evaluated in a bleomycin mouse model using histology, immunostaining, qRT-PCR, and tissue glutamine quantification.
    Findings: We identified an S100A8+S100A9+ transitional macrophage population bridging FABP4+MME+ precursors and SPP1+MMP9+ effector macrophages. These cells are associated with fibrogenesis through two interconnected mechanisms: epidermal growth factor (EGF)-mediated signaling to fibroblasts and alveolar epithelial cells, amplifying profibrotic communication; and glutamine metabolic reprogramming, marked by GLUL. In murine bleomycin induced fibrosis, these features emerged early, with elevated S100a8, S100a9, Fcna, Timp1, and coincided with increased lung glutamine at day 14. Peripheral profiling confirmed concordant transcriptional changes in monocytes from IPF patients, supporting translational biomarker potential.
    Interpretation: S100A8+S100A9+ macrophages might function as a druggable immune-metabolic hub in IPF. Targeting S100A8/A9, GLUL-dependent glutamine flux, or EGFR-axis signaling may enable actionable paths for mechanism-based therapy. Furthermore, their peripheral molecular signature may offer a disease-specific biomarker platform for early diagnosis and therapeutic monitoring.
    Keywords:  EGF/AREG signaling; S100A8/S100A9; glutamine metabolism/GLUL; idiopathic pulmonary fibrosis; macrophage heterogeneity; transitional macrophages
    DOI:  https://doi.org/10.3389/fimmu.2026.1832940
  7. Transl Cancer Res. 2026 Jun 30. 15(6): 462
    Effects of different drugs on radiation-induced oral mucositis in patients with head and neck tumors: a systematic review and a network meta-analysis.
    Shuchang Lou, Xiaodong Li, Lingyun Shi.
       Background: Radiation-induced oral mucositis (RIOM) affects over 80% of head and neck tumor patients receiving radiotherapy. Although four routine drugs are used for RIOM prevention, direct comparative evidence across multiple clinical outcomes remains insufficient. This study aims to compare the effects of different pharmacological interventions on RIOM in patients with head and neck tumors via a network meta-analysis of RIOM.
    Methods: We conducted systematic searches in PubMed, EMBASE, and the Cochrane Library for RCTs published up to August 31, 2025. The search only included articles in English. We selected studies that reported on the severity of RIOM, the duration of RIOM, the severity of xerostomia, and the severity of dysphagia as primary outcomes. Secondary outcomes included the use of analgesics, weight loss, the use of feeding tubes, and treatment interruptions. We used the Risk-of-Bias 2 (RoB2) tool to assess the risk of bias in the included studies and performed a network meta-analysis by using Stata 17.0 software.
    Results: A total of 29 RCTs involving 2,276 patients were included. Glutamine significantly reduced the risk of RIOM compared with amifostine [odds ratio (OR) =0.13, 95% confidence interval (CI): 0.03-0.51] and cytokine (OR =0.31, 95% CI: 0.10-0.99). Compared with amifostine, cytokine (OR: 10.28, 95% CI: 1.09, 96.79), glutamine (OR: 11.09, 95% CI: 1.10, 112.24), and placebo (OR: 12.54, 95% CI: 1.41, 111.20) all significantly increased the number of cases with dysphagia. Glutamine reduced the number of patients requiring feeding tubes and the incidence of treatment interruptions. However, no significant differences were found in the duration of RIOM, the number of reported ≥ grade II xerostomia cases, the number of patients using analgesics, or weight loss.
    Conclusions: Glutamine is recommended as the preferred pharmacological intervention for preventing and alleviating RIOM in head and neck cancer patients, particularly in reducing the risks of severe RIOM, incidence of feeding tube, and treatment interruptions. Additionally, amifostine significantly reduced the risk of dysphagia. Based on the current evidence, glutamine appears to offer the most favorable risk-benefit profile among the evaluated interventions. Future research should adopt well-designed, unified RCTs with adequate samples to revalidate whether these measures are truly effective and safe.
    Keywords:  Radiation-induced oral mucositis; glutamine; head and neck cancer; network meta-analysis; randomized controlled trials (RCTs)
    DOI:  https://doi.org/10.21037/tcr-2026-0538
  8. Front Med (Lausanne). 2026 ;13 1809188
    Exploring potential associations between blood metabolites and cirrhosis risk: a Mendelian randomization and LC-MS/MS analysis.
    Duoduo Lv, Ning Han, Hong Tang.
       Background: The development of cirrhosis is closely intertwined with metabolic processes. No previous studies have reported a clear causal relationship between cirrhosis and metabolic processes. Thus, this study aimed to explore the potential associations between blood metabolites and cirrhosis using Mendelian randomization (MR) combined with targeted metabolomics analysis.
    Methods: A two-sample MR analysis was conducted using genome-wide association study data to evaluate the associations of circulating metabolites with cirrhosis. Statistical evaluations employed inverse variance-weighted models, MR-Egger regression, and sensitivity tests addressing pleiotropy and heterogeneity. In addition, blood samples were collected from 10 patients with liver cirrhosis and 10 healthy controls. Blood amino acid concentrations were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to provide preliminary clinical evidence supporting the MR-derived candidate metabolites.
    Results: The MR analysis found that increases in 11 metabolites/metabolic ratios were associated with elevated risks of liver cirrhosis, whereas increases in the remaining 3 metabolites were related to the prevention of the occurrence of liver cirrhosis. Among these candidates, genetically predicted higher glutamine degradant levels were associated with a lower risk of cirrhosis (OR = 0.877, 95% CI: 0.784-0.981, p = 0.022). Pathway analysis further suggested that arginine biosynthesis, proline metabolism, and nitrogen metabolism may be involved in metabolic alterations related to cirrhosis. The LC-MS/MS analysis showed lower glutamate and glutathione levels in patients with cirrhosis than in controls, providing preliminary support for altered glutamine-related metabolism in cirrhosis.
    Conclusion: This study provides suggestive MR evidence linking specific circulating metabolites to cirrhosis risk. In particular, glutamine-related metabolic alterations may be associated with susceptibility to cirrhosis. These findings provide exploratory insights into metabolite-related pathways that may contribute to cirrhosis development.
    Keywords:  LC–MS/MS; Mendelian randomization; cirrhosis; glutamine; metabolites
    DOI:  https://doi.org/10.3389/fmed.2026.1809188
  9. Adv Exp Med Biol. 2026 ;1501 619-646
    Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.
    José Alberto Rodrigues, João Serra Lopes.
      Cancer metabolism is characterized by extensive reprogramming of biochemical pathways, enabling malignant cells to sustain proliferation, adapt to fluctuating environments, and resist therapeutic stress. While the Warburg effect has long been considered a hallmark of cancer, recent evidence highlights the dynamic metabolic plasticity of tumor cells, which flexibly engage glycolysis, oxidative phosphorylation, glutaminolysis, and lipid biosynthesis depending on nutrient availability and microenvironmental conditions. These adaptations not only promote tumor survival but also generate exploitable metabolic vulnerabilities. Mathematical and computational modeling have become a powerful strategy for unraveling this complexity and translating biological insights into clinical applications. Kinetic models offer a mechanistic resolution of enzymatic flux control, while constraint-based frameworks such as flux balance analysis enable genome-scale prediction of steady-state flux distributions and identification of metabolic liabilities. Agent-based models extend this analysis to capture spatial heterogeneity, tumor-immune interactions, and emergent behaviors within the tumor microenvironment. More recently, machine learning and hybrid data-driven approaches have complemented mechanistic modeling by integrating high-dimensional multi-omics datasets to reveal biomarker patterns, predict therapeutic response, and stratify patients according to metabolic phenotype. Personalized genome-scale metabolic models, constructed from patient-specific omics data, have demonstrated the ability to predict individual vulnerabilities and guide the selection of metabolism-based therapies. Hybrid frameworks such as physics-informed neural networks and neural ordinary differential equations further extend predictive capacity to capture tumor-immune-metabolism dynamics. Collectively, these approaches bridge preclinical experimentation and translational oncology by enabling virtual hypothesis testing, biomarker discovery, and rational design of adaptive therapeutic strategies. By uniting mechanistic insights with predictive modeling, mathematical frameworks are poised to become integral to precision oncology. Their integration into clinical pipelines will accelerate the identification of metabolic targets, improve patient stratification, and advance the development of effective, personalized metabolism-based cancer therapies.
    Keywords:  Agent-based models; Cancer metabolism; Flux balance analysis; Genome-scale metabolic models; Kinetic models; Machine learning; Mathematical modeling; Metabolism-based therapy; Multi-omics integration; Precision oncology
    DOI:  https://doi.org/10.1007/978-3-032-12166-0_23
  10. J Proteome Res. 2026 Jul 15.
    Serum 1H NMR-Based Untargeted Metabolomics for Noninvasive Early Detection of Breast Cancer.
    Akbar Ali, Tengku Ahmad Damitri Al Astani Tengku Din, Maulidiani Maulidiani, Heba Mohammed Arafat, Wan Faiziah Wan Abdul Rahman, Maya Mazuwin Yahya, Suhaini Kadiman, Ras Azira Binti Ramli, Wan Norlina Wan Azman.
      Breast cancer (BC) is the most common malignancy among women, and late-stage presentation remains common in Asia, highlighting the need for affordable and minimally invasive adjuncts to imaging. This study evaluated whether fasting serum 1H NMR metabolomics could distinguish Malaysian women with newly diagnosed treatment-naïve BC from healthy controls and identify perturbed metabolic pathways. After spectral quality control exclusion, 101 participants were analyzed (BC, n = 44; control, n = 57). PCA and oOPLS-DA showed group separation, and 14 metabolites were deferred significantly after false discovery rate correction. Endogenous metabolites evaluated in BC included 3-hydroxybutyrate, citrate, glutamine, malonate, phenylalanine, dimethyl sulfone, myo-inositol, and L-carnitine, and the levels of formate, tyrosine, histamine, and p-hydroxyphenylacetate were reduced. Acetylsalicylate and salicylate were classified as exogenous aspirin-related markers and were excluded from the endogenous biomarker panel. Pathway analysis indicated disruptions in the tricarboxylic acid (TCA) cycle, glyoxylate/dicarboxylate metabolism, butanoate metabolism, and amino acid pathways. Metabolite-based classifiers achieved 0.901 accuracy on an internal held-out test partition (AUC 0.942) and 0.902 ± 0.061 accuracy in repeated stratified 5-fold cross-validation. These internally validated findings define a BC-associated serum metabolomic signature in a Southeast Asian cohort and warrant independent external validation.
    Keywords:  1H NMR spectroscopy; biomarkers; breast cancer; machine learning; serum metabolomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c01124
  11. Int J Mol Sci. 2026 Jun 30. pii: 5912. [Epub ahead of print]27(13):
    Branched-Chain and Aromatic Amino Acids Mark Early Metabolic Shifts in Adults with Varying Adiposity.
    Marta Jaskulak, Iwona Rybakowska, Magdalena Gregorczyk, Klaudia Antoniak-Pietrynczak, Anna Sośnicka, Patrycja Jabłońska, Katarzyna Zorena.
      Amino acid metabolism has been increasingly recognized as a central determinant of obesity and insulin resistance, yet the specific contributions of individual amino acids require further clarification. The aim of the study was to detect relationships between serum amino acid concentrations and metabolic parameters in overweight and obese individuals. Amino acid concentrations were measured in 50 individuals classified as normal weight, overweight, or obese, and were analyzed using principal component analysis (PCA), K-means clustering, multiple linear regression, and Random Forest models. Obese individuals exhibited markedly elevated levels of branched-chain amino acids (BCAAs: valine, isoleucine, leucine) and glutamic acid, accompanied by reduced concentrations of serine, glycine, and glutamine, compared with normal weight participants. PCA revealed that the first component, which explained 35.5% of the total variance, was driven primarily by BCAAs, serine, and glutamine, while the second component, accounting for 9.5% of variance, was influenced by threonine, tryptophan, and asparagine. The multiple linear regression model explained 89.6% of the variance in HOMA-IR (R2 = 0.896, p < 0.001), with isoleucine emerging as the strongest positive predictor (p < 0.001), valine and leucine showing additional significant associations (p = 0.035), and tyrosine demonstrating a significant negative association (p = 0.039), while proline was not significant. The Random Forest model predicting insulin resistance achieved robust cross-validated performance (R2 = 0.86 ± 0.06), with valine, isoleucine, and leucine accounting for the majority of predictive importance, followed by tyrosine and glutamine. Together, these findings demonstrate that amino acid profiling provides powerful discriminatory and predictive capacity for insulin resistance and obesity. BCAAs consistently emerged as the most important predictors across complementary analytical frameworks, confirming their central role in metabolic dysregulation, while glycine appeared to exert a potential protective effect. The identification of a metabolically overweight subgroup underscores the heterogeneity of the overweight state and highlights the utility of amino acid profiling for early risk stratification and the development of targeted interventions.
    Keywords:  HOMA-IR; amino acids; insulin resistance; liquid chromatography-mass spectrometry; obesity
    DOI:  https://doi.org/10.3390/ijms27135912
  12. J Chem Inf Model. 2026 Jul 17.
    Pan-Cancer Landscape of Spatial PTM and Mutation Clusters Reveals Allosteric Regulation of Protein-Protein Interactions.
    Dongfang Luo, Jiani Zheng, Xikun Du, Danni Xia, Meng Zhang, Zhongjie Liang.
      Post-translational modifications (PTMs) and somatic mutations are pervasive in cancer, yet their three-dimensional organization and coordinated regulatory mechanisms within protein-protein interactions (PPIs) remain poorly understood. Here we develop ClusTar, a structural framework that systematically identifies spatial clusters of PTMs and mutations within PPIs and links them to protein dynamics and functional regulation. Integrating TCGA genomics, CPTAC proteomics, and structural PPI data across ten cancer types, we identify 3,966 high-confidence PTM-mutation clusters that are enriched in functional residues and frequently overlap with ligand-binding pockets. Dynamics analyses reveal that many clusters are mechanically coupled to PPI interfaces and mediate long-range allosteric communications. In RhoA complexes, glutamine substitutions at acetylated lysine sites (K7Q, K18Q, K104Q and K118Q/K162Q) have been observed to redistribute key network hubs and rewire allosteric communication pathways among clustered residues. Experimental validation demonstrates that these mutations strengthen protein interactions and promote breast cancer cell migration, highlighting their potential as allosteric hotspots. Together, our results establish a pan-cancer structural atlas of PTM-mutation clusters and highlight allosteric hotspots as potential targets for modulating PPIs.
    DOI:  https://doi.org/10.1021/acs.jcim.6c01767
  13. Science. 2026 Jul 16. 393(6808): eadx8675
    A transcriptional biosensor reveals mechanisms of α-ketoglutarate signaling to chromatin.
    Alex C Sternisha, Haocheng Li, Kumar Gajendra, Yi Xiao, Xin Zhao, Jeffrey I Traylor, Lei Guo, Ji Hyung Jun, Morgan Fleishman, Tracey Shipman, Vinesh T Puliyappadamba, Pranita Kaphle, Qing Ouyang, Michael Schmidt, Diana D Shi, Milan R Savani, Alexander C-Y Tsai, Joyce H Lee, Ruth Gordillo, Javier Garcia-Bermudez, Yoon Jung Kim, Shih-Chia Tso, Chad A Brautigam, Lauren G Zacharias, Thomas P Mathews, Lin Xu, John G Doench, Vidyasagar Koduri, Kalil G Abdullah, Michalis Agathocleous, Laura A Banaszynski, Ralph J DeBerardinis, Eric M Morrow, Samuel K McBrayer.
      The metabolite α-ketoglutarate (αKG) is required for chromatin demethylation, but mechanisms that control αKG abundance in the nucleus are poorly defined. We designed a biosensor to monitor this metabolite pool in human cells using an αKG-responsive cyanobacterial transcription factor, NtcA, and used it to identify genes that regulate αKG in the nucleus. We defined an interorganelle pathway in which sequential mitochondrial activities of glutamic-pyruvic transaminase 2 (GPT2) and the SLC25A11 transporter supply nuclear αKG. In a mouse model of GPT2 deficiency, an inborn error of metabolism, Gpt2 loss caused histone hypermethylation in the brain and dysregulated neurodevelopmental genes. Restoring αKG counteracted these changes and promoted mouse fitness. Our work provides a tool to directly monitor nuclear αKG and reveals nuclear αKG depletion as a key pathogenic mechanism underlying GPT2 deficiency.
    DOI:  https://doi.org/10.1126/science.adx8675
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