Distinct Metabolic Phenotypes Research Articles

TMET-02. CELL-INTRINSIC METABOLIC PHENOTYPES IDENTIFIED IN GLIOBLASTOMA PATIENTS USING MASS SPECTROMETRY IMAGING OF 13C-LABELED GLUCOSE METABOLISM

Abstract Glioblastoma (GB) is an inherently heterogeneous and invasive primary brain cancer. Genomic and transcriptomic studies have attempted to classify GB into subtypes that predict survival and that have different therapeutic vulnerabilities. An outstanding question and technical challenge is to visualize the metabolism of a tumour within its native microenvironment and understand to what extent its metabolism is driven by the microenvironment. Patients with GB tumours were infused with [U-13C]glucose and intra-operative multi-regional tumour samples were rapidly snap frozen for analysis using mass spectrometry imaging (MSI). Tissue microenvironment was assessed using multiplexed antibodies in immunocytochemistry (IMC) of contiguous sections. Isotope tracking in human-derived neurospheres and patient-derived xenografts were used to confirm the presence of metabolic phenotypes detected in the human samples and to test the susceptibility of each phenotype to a panel of drugs targeting cellular metabolism. Three metabolic signatures were identified in patient tumours: glycolytic, oxidative and a mixed glycolytic/oxidative phenotype. The phenotypes did not correlate with microenvironmental features, including proliferation rate, immune cell infiltration, and vascularisation. The phenotypes were retained when patient-derived cells were grown in vitro, or as orthotopically implanted xenografts, and were robust to changes in oxygen concentration, demonstrating their cell intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes are large enough to be detected using clinically applicable metabolic imaging techniques. The phenotypes also demonstrate differential drug sensitivities, suggesting imaging of these phenotypes may be used to stratify patients for personalised therapy trials. This is the first report to demonstrate high resolution imaging of metabolic fluxes in a human tumour in vivo and the presence of different metabolic phenotypes within GB that are tumour cell intrinsic and largely independent of the tumour microenvironment.

Association of metabolic phenotypes with cardiovascular events in primary and secondary prevention

Abstract Background Metabolic disorders are established risk factors for the development of coronary artery disease (CAD) and major adverse cardiovascular events (MACE). Although obesity is strongly related with the metabolic health status, its role as a cardiovascular risk modifier in the absence of metabolic disorders remains controversial. Recent implementation of nutrient-stimulated hormone-based therapies (NUSH) including glucagon-like peptide-1 (GLP-1) receptor agonists in the treatment of obesity even in metabolically healthy individuals requires further understanding to ensure optimal patient management. Purpose The aim of this study is to investigate the association of metabolic phenotypes with cardiovascular events in primary and secondary prevention of CAD. Methods We included patients over 18 years electively evaluated for CAD by invasive coronary angiography (ICA) between 2010 and 2021 in our tertiary referral centre in one of Europe’s largest university hospitals. Metabolic disorder was considered as the presence of diabetes, irrespective of additional risk factors, or hypertension and hyperlipidaemia, and obesity as a BMI of ≥30 kg/m², distinguishing four metabolic phenotypes: metabolically healthy/unhealthy nonobese/obese (MHN, MHO, MUN, MUO). The primary study endpoint MACE was defined as a composite of cardiovascular death, non-fatal myocardial infarction, ischemic stroke, and hospitalization for heart failure. Results The total study population included 12 760 patients (68 [58-76] years; 57.3% male), of whom 56.5% presented metabolically healthy (43.3% MHN; 13.1% MHO) and 43.5% unhealthy (28.3% MUN; 15.2% MUO). During a median follow up time of 3.91 (1.75-7.09) years, 2 592 (20.3%) MACE and 3 351 (26.3%) all-cause deaths occurred. Cox regression analysis adjusted for age, sex, and chronic kidney disease (CKD) showed different risk patterns for distinct metabolic phenotypes (Table 1). While we observed higher event rates in metabolically unhealthy compared to healthy individuals, obesity alone was not associated with increased events (Figure 1). However, metabolically healthy individuals experienced lower event rates with increasing BMI. Among patients without or with nonobstructive CAD, metabolic disease was strongly associated with increased subsequent coronary revascularization regardless of BMI. These patterns were similar across all endpoints irrespective of presence of obstructive or nonobstructive CAD. Conclusions Whereas metabolic disorders are strongly associated with adverse clinical events, obesity alone does not reflect the cardiovascular risk profile in patients with or without obstructive CAD. Thus, focusing on obesity alone for primary or secondary prevention appears unjustified. Particularly after the recent implementation of GLP-1 receptor agonists for the treatment of obesity, precise and individual risk stratification is essential to allow for an optimal personalized patient management.Figure 1Table 1

Metabolites and Lipoproteins May Predict the Severity of Early Acute Pancreatitis in a South African Cohort.

Background: Acute pancreatitis (AP) can be life-threatening with unpredictable severity. Despite advances in management, its pathogenesis remains unclear. This study investigated metabolites and lipoprotein profiles in AP patients of African descent to understand the underlying pathophysiological conditions so as to inform prognosis and management. Methods: Serum samples were collected from 9 healthy controls (HCs) and 30 AP patients (8 with mild AP, 14 with moderately severe AP, and 8 with severe AP) on days 1, 3, 5, and 7 post epigastric pain and subjected to nuclear magnetic resonance (NMR) spectroscopy. Wilcoxon and Kruskal-Wallis rank-sum tests compared numerical covariates. Lipoprotein characterization was performed using the Liposcale test, and Spearman's rank test assessed data correlations. The p-values < 0.05 indicated significance. Results: Thirty-eight metabolic signals and information on lipoprotein subclasses were identified from the NMR spectra. The severity of AP correlated with increased levels of 3-hydroxybutyrate and acetoacetate and decreased levels of ascorbate. Distinct metabolic phenotypes were identified and characterized by unique inflammatory and lipoprotein profiles. High-density lipoprotein cholesterol (HDL-C) decreased across all the metabolic phenotypes of AP when compared with the HC, while elevated immediate density lipoprotein cholesterol (IDL-C) and very low-density lipoprotein cholesterol (VLDL-C) levels were observed. Time-dependent changes in metabolites were indicative of responsiveness to therapy. Conclusions: Our findings indicate that dysregulated metabolites and lipoproteins can be used to differentiate AP disease state and severity. Furthermore, integrating clinical parameters with data on metabolic and lipoprotein perturbations can contribute to a better understanding of the complex pathophysiology of AP.

CELL-INTRINSIC METABOLIC PHENOTYPES IDENTIfiED IN GLIOBLASTOMA PATIENTS USING MASS SPECTROMETRY IMAGING OF 13C-LABELED GLUCOSE METABOLISM

Abstract AIMS Glioblastoma (GB) is an inherently heterogenous and invasive primary brain tumour. Genetic and transcriptomic studies have attempted to classify GB into subtypes that can identify therapeutic vulnerabilities and predict survival. An outstanding question and technical challenge is to visualise the metabolism of a tumour within its native microenvironment and understand to what extent its metabolism is driven by the microenvironment. METHOD Patients with GB tumours were infused with [U-13C]glucose and intra-operative multi-regional tumour samples were rapidly snap frozen for metabolic tracing analysis using mass spectrometry imaging (MSI). Tissue microenvironment was assessed using multiplexing of antibodies on immunocytochemistry (IMC) of parallel sections. Isotope tracing in human derived neurospheres and patient derived xenografts were used to confirm the presence of metabolic phenotypes detected in the human samples and to test susceptibility of each phenotype to a panel of drugs targeting cellular metabolism. RESULTS Three metabolic signatures were identified in patient GB tumours: glycolytic, oxidative and a mixed glycolytic/oxidative phenotype. The phenotypes did not correlate with microenvironmental features, including proliferation rate, immune cell infiltration, and vascularisation. The phenotypes were retained when patient derived cells were grown in vitro, or as orthotopically implanted xenografts, and were robust to changes in oxygen concentration, demonstrating their cell intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes are large enough to be detected using clinically applicable metabolic imaging techniques. The phenotypes also demonstrate differential drug sensitivities. CONCLUSION This is the first report to demonstrate high resolution imaging of metabolic fluxes in a human tumour in vivo. In conjunction with studies on patient-derived neurospheres and orthotopically implanted xenografts we have demonstrated the presence of different metabolic phenotypes within GB that are tumour cell intrinsic and largely independent of the tumour microenvironment. These can be imaged clinically and used to stratify patients for personalised therapy.

Metabolic phenotypes and vitamin D response in the critically ill: A metabolomic cohort study

Background & AimsAlthough vitamin D deficiency is common in critically ill patients, randomized controlled trials fail to demonstrate benefits of supplementation. We aimed to identify distinct vitamin D3 responsive metabolic phenotypes prior to trial intervention of high-dose vitamin D3 by applying machine learning clustering method to metabolomics data from the Correction of Vitamin D Deficiency in Critically Ill Patients (VITdAL-ICU) trial. MethodsIn the randomized, placebo-controlled VITdAL-ICU trial, critically ill adults received placebo or high-dose vitamin D3. To distinguish vitamin D3 responsive metabolic phenotypes prior to intervention, we implemented consensus clustering with partitioning around medoids algorithm to the plasma metabolome data before randomization. Individual metabolite differences were determined utilizing linear mixed-effects regression models stratified for metabolomic phenotypes with false discovery rate adjustment. The association between vitamin D3 supplementation and 180-day mortality was evaluated in each metabolic phenotype, applying multivariable logistic regression analysis. ResultsIn 453 critically ill adults, the study identified 4 distinct metabolic phenotypes (clusters A. N=134; B. N=123; C. N=92; D. N=104). We found differential metabolic pathway patterns in the four clusters. Specifically, branched chain amino acid catabolic metabolites, long-chain acylcarnitines and diacylglycerol species are significantly increased in a specific metabolic phenotype (cluster D) following high-dose vitamin D3. Further, in cluster D high-dose vitamin D3 supplementation had a significantly lower adjusted odds of 180-day mortality after controlling age, sex, Simplified Acute Physiology Score II, admission diagnosis, and baseline 25-hydroxyvitamin D (OR 0.28 (95%CI, 0.09–0.89); P=0.03). In metabotype A, B, and C, high-dose vitamin D3 supplementation was not significantly associated with lower 180-day mortality following multivariable adjustment. ConclusionIn this post-hoc cohort study of the VITdAL-ICU trial, the clustering analysis of plasma metabolome data identified biologically distinct metabolic phenotypes. Among clusters, we found the different associations between high-dose vitamin D3 supplementation and specific metabolite pathways as well as 180-day mortality. Our findings facilitate further research to validate metabolic phenotype-targeted strategies for critical illness treatments.

Integrating Spectral Sensing and Systems Biology for Precision Viticulture: Effects of Shade Nets on Grapevine Leaves

This study investigates how grapevines (Vitis vinifera L.) respond to shading induced by artificial nets, focusing on physiological and metabolic changes. Through a multidisciplinary approach, grapevines’ adaptations to shading are presented via biochemical analyses and hyperspectral data that are then combined with systems biology techniques. In the study, conducted in a ‘Moscatel Galego Branco’ vineyard in Portugal’s Douro Wine Region during post-veraison, shading was applied and predawn leaf water potential (Ψpd) was then measured to assess water stress. Biochemical analyses and hyperspectral data were integrated to explore adaptations to shading, revealing higher chlorophyll levels (chlorophyll a-b 117.39% higher) and increased Reactive Oxygen Species (ROS) levels in unshaded vines (52.10% higher). Using a self-learning artificial intelligence algorithm (SL-AI), simulations highlighted ROS’s role in stress response and accurately predicted chlorophyll a (R2: 0.92, MAPE: 24.39%), chlorophyll b (R2: 0.96, MAPE: 17.61%), and ROS levels (R2: 0.76, MAPE: 52.17%). In silico simulations employing flux balance analysis (FBA) elucidated distinct metabolic phenotypes between shaded and unshaded vines across cellular compartments. Integrating these findings provides a systems biology approach for understanding grapevine responses to environmental stressors. The leveraging of advanced omics technologies and precise metabolic models holds immense potential for untangling grapevine metabolism and optimizing viticultural practices for enhanced productivity and quality.

The two alternative NADH:quinone oxidoreductases from Staphylococcus aureus: two players with different molecular and cellular roles.

Staphylococcus aureus is an opportunistic pathogen that has emerged as a major public health threat due to the increased incidence of its drug resistance. S. aureus presents a remarkable capacity to adapt to different niches due to the plasticity of its energy metabolism. In this work, we investigated the energy metabolism of S. aureus, focusing on the alternative NADH:quinone oxidoreductases, NDH-2s. S. aureus presents two genes encoding NDH-2s (NDH-2A and NDH-2B) and lacks genes coding for Complex I, the canonical respiratory NADH:quinone oxidoreductase. This observation makes the action of NDH-2s crucial for the regeneration of NAD+ and, consequently, for the progression of metabolism. Our study involved the comprehensive biochemical characterization of NDH-2B and the exploration of the cellular roles of NDH-2A and NDH-2B, utilizing knockout mutants (Δndh-2a and Δndh-2b). We show that NDH-2B uses NADPH instead of NADH, does not establish a charge-transfer complex in the presence of NADPH, and its reduction by this substrate is the catalytic rate-limiting step. In the case of NDH-2B, the reduction of the flavin is inherently slow, and we suggest the establishment of a charge transfer complex between NADP+ and FADH2, as previously observed for NDH-2A, to slow down quinone reduction and, consequently, prevent the overproduction of reactive oxygen species, which is potentially unnecessary. Furthermore, we observed that the lack of NDH-2A or NDH-2B impacts cell growth, volume, and division differently. The absence of these enzymes results in distinct metabolic phenotypes, emphasizing the unique cellular roles of each NDH-2 in energy metabolism.IMPORTANCEStaphylococcus aureus is an opportunistic pathogen, posing a global challenge in clinical medicine due to the increased incidence of its drug resistance. For this reason, it is essential to explore and understand the mechanisms behind its resistance, as well as the fundamental biological features such as energy metabolism and the respective players that allow S. aureus to live and survive. Despite its prominence as a pathogen, the energy metabolism of S. aureus remains underexplored, with its respiratory enzymes often escaping thorough investigation. S. aureus bioenergetic plasticity is illustrated by its ability to use different respiratory enzymes, two of which are investigated in the present study. Understanding the metabolic adaptation strategies of S. aureus to bioenergetic challenges may pave the way for the design of therapeutic approaches that interfere with the ability of the pathogen to successfully adapt when it invades different niches within its host.

Dynamics of DNA methylation during osteogenic differentiation of porcine synovial membrane mesenchymal stem cells from two metabolically distinct breeds

ABSTRACT Mesenchymal stem cells (MSCs), with the ability to differentiate into osteoblasts, adipocytes, or chondrocytes, show evidence that the donor cell’s metabolic type influences the osteogenic process. Limited knowledge exists on DNA methylation changes during osteogenic differentiation and the impact of diverse donor genetic backgrounds on MSC differentiation. In this study, synovial membrane mesenchymal stem cells (SMSCs) from two pig breeds (Angeln Saddleback, AS; German Landrace, DL) with distinct metabolic phenotypes were isolated, and the methylation pattern of SMSCs during osteogenic induction was investigated. Results showed that most differentially methylated regions (DMRs) were hypomethylated in osteogenic-induced SMSC group. These DMRs were enriched with genes of different osteogenic signalling pathways at different time points including Wnt, ECM, TGFB and BMP signalling pathways. AS pigs consistently exhibited a higher number of hypermethylated DMRs than DL pigs, particularly during the peak of osteogenesis (day 21). Predicting transcription factor motifs in regions of DMRs linked to osteogenic processes and donor breeds revealed influential motifs, including KLF1, NFATC3, ZNF148, ASCL1, FOXI1, and KLF5. These findings contribute to understanding the pattern of methylation changes promoting osteogenic differentiation, emphasizing the substantial role of donor the metabolic type and epigenetic memory of different donors on SMSC differentiation.

Adipocyte Mitochondria: Deciphering Energetic Functions across Fat Depots in Obesity and Type 2 Diabetes.

Adipose tissue, a central player in energy balance, exhibits significant metabolic flexibility that is often compromised in obesity and type 2 diabetes (T2D). Mitochondrial dysfunction within adipocytes leads to inefficient lipid handling and increased oxidative stress, which together promote systemic metabolic disruptions central to obesity and its complications. This review explores the pivotal role that mitochondria play in altering the metabolic functions of the primary adipocyte types, white, brown, and beige, within the context of obesity and T2D. Specifically, in white adipocytes, these dysfunctions contribute to impaired lipid processing and an increased burden of oxidative stress, worsening metabolic disturbances. Conversely, compromised mitochondrial function undermines their thermogenic capabilities, reducing the capacity for optimal energy expenditure in brown adipocytes. Beige adipocytes uniquely combine the functional properties of white and brown adipocytes, maintaining morphological similarities to white adipocytes while possessing the capability to transform into mitochondria-rich, energy-burning cells under appropriate stimuli. Each type of adipocyte displays unique metabolic characteristics, governed by the mitochondrial dynamics specific to each cell type. These distinct mitochondrial metabolic phenotypes are regulated by specialized networks comprising transcription factors, co-activators, and enzymes, which together ensure the precise control of cellular energy processes. Strong evidence has shown impaired adipocyte mitochondrial metabolism and faulty upstream regulators in a causal relationship with obesity-induced T2D. Targeted interventions aimed at improving mitochondrial function in adipocytes offer a promising therapeutic avenue for enhancing systemic macronutrient oxidation, thereby potentially mitigating obesity. Advances in understanding mitochondrial function within adipocytes underscore a pivotal shift in approach to combating obesity and associated comorbidities. Reigniting the burning of calories in adipose tissues, and other important metabolic organs such as the muscle and liver, is crucial given the extensive role of adipose tissue in energy storage and release.

Exploring the Interactions between Obesity and Diabetes: Implications for Understanding Metabolic Dysregulation in a Saudi Arabian Adult Population.

The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants (n = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.99 kg/m2) with (n = 57) and without diabetes (n = 58) or overweight/obese (BMI > 24.99 kg/m2) with (n = 102) and without diabetes (n = 72). Distinct plasma metabolic phenotypes associated with high BMI and diabetes were identified using nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography mass spectrometry. Increased plasma glucose and dysregulated lipoproteins were characteristics of obesity in individuals with and without diabetes, but the obesity-associated lipoprotein phenotype was partially masked in individuals with diabetes. Although there was little difference between diabetics and nondiabetics in the global plasma LDL cholesterol and phospholipid concentration, the distribution of lipoprotein particles was altered in diabetics with a shift toward denser and more atherogenic LDL5 and LDL6 particles, which was amplified in the presence of obesity. Further investigation is warranted in larger Middle Eastern populations to explore the dysregulation of metabolism driven by interactions between obesity and diabetes in young adults.

Clustering polycystic ovary syndrome laboratory results extracted from a large internet forum with machine learning

BackgroundPolycystic Ovary Syndrome (PCOS) is reported to affect between 4% and 21% of reproductive aged people with ovaries. It is a heterogeneous condition with a lack of established phenotypes that address the range of reproductive and metabolic features present in PCOS. These reproductive and metabolic features may result in patients undergoing a variety of relevant laboratory tests. Previous work has led to the gathering of laboratory test results from a PCOS specific forum, hosted on a website called reddit. ObjectivesIn this paper, laboratory results and body mass index (BMI) posted on the PCOS reddit forum were clustered to show the usefulness of the PCOS forum for PCOS research and validate existing PCOS phenotypes or discover other appropriate phenotypes. Methods and resultsOver 1500 sets of PCOS-related reddit laboratory test results and BMIs were clustered using nearest neighbour imputation and K-means clustering. However, only non-imputed data was included in the final clusters. Kernel Density Estimation plots were used to display the distinct clusters. The clustered test results suggested the existence of distinct metabolic and reproductive phenotypes, as well as a group displaying mild features of both types of dysregulations and a group skewed towards normal results. It was also possible to separate the groups further into distinct hypothyroid groups within the mixed dysregulation group and to separate insulin resistant and diabetes-like groups within the metabolic group. ConclusionsThis research further validates the usefulness of exploring alternate data sources in the age of the internet and machine learning. The reddit clusters reinforced the existing notion that people with PCOS can be separated into a primarily metabolic pathology group, a primarily reproductive pathology group and an in between group with pathology in both domains.

Low-Attenuation Coronary Plaque Volume and Cardiovascular Events in Patients with Distinct Metabolic Phenotypes with or without Diabetes.

Diabetes mellitus (DM) plays a key role in the pathophysiology of metabolic syndrome (MetS). This study aimed to investigate the association among DM, low-attenuation plaque (LAP) volume, and cardiovascular outcomes across metabolic phenotypes in patients with suspected coronary artery disease (CAD) who underwent coronary computed tomography angiography (CCTA). We included 530 patients who underwent CCTA. MetS was defined as the presence of a visceral adipose tissue area 100 in patients with DM (n = 58) or two or more MetS components excluding DM (n = 114). The remaining patients were categorised as non-MetS patients with DM (n = 52) or without DM (n = 306). A CCTA-based high-risk plaque was defined as a LAP volume of 4%. The primary endpoint was the presence of a major cardiovascular event (MACE), which was defined as a composite of cardiovascular death, acute coronary syndrome, and coronary revascularization. The incidence of MACE was the highest in the non-MetS with DM group, followed hierarchically by the MetS with DM, MetS without DM, and non-MetS without DM groups. In the multivariable Cox hazard model analysis, DM as a predictor was associated with MACE independent of LAP volume 4% (hazard ratio, 2.68; 95% confidence interval, 1.16-6.18; p = 0.02), although MetS did not function as an independent predictor. A LAP volume 4% functioned as a predictor of MACE, independent of each metabolic phenotype or DM. This study demonstrated that DM, rather than MetS, is a predictor of coronary events independent of high-risk plaque volume in patients who underwent CCTA.

The role of epigenetic modifications in the formation of heterogeneous phenotypes in diabetes mellitus (a literature review)

This review article provides a summary and update on the role of epigenetic mechanisms in predisposition and progression of diabetes, analyzes the data concerning the cause-and-effect relationship between epigenetic changes and the emergence of distinct metabolic phenotypes. Extensive genetic research has enabled the isolation of a group of genes associated with a high risk of developing diabetes. However, numerous data point to the key role of so-called epigenetic modifications in the interaction between genes and the environment, which arise during ontogenesis based on the existing genotype under the influence of external factors. These modifications do not affect the primary DNA sequence, but influence gene expression through chemical modification and alteration of the secondary structure of DNA molecules and chromatin. Epigenetic mechanisms can program pathological phenotypes in subsequent generations. The main molecular mechanisms of epigenetic modifications are DNA methylation, histone and miRNA modification. Changes in the expression of genes that ensure the synthesis of key enzymes and regulatory molecules lead to disruption in the main signaling metabolic pathways. Deregulation of genes responsible for inflammatory, atherosclerotic and other pathological processes, in particular, leads to endothelial dysfunction and development of diabetic complications, such as cardiovascular diseases, diabetic nephropathy, retinopathy, neuropathy. Hyperglycemia, oxidative stress, inflammatory factors are known as mediators in the pathogenesis of type 2 diabetes and its complications. Since epigenetic modifications are reversible, the methylation process can be influenced by exercise, dietary, lifestyle changes and pharmacological agents such as methyl group donors. For example, S-adenosylmethionine, through participation in methylation reactions, can modulate the folate cycle function and production of homocysteine, an endothelium-toxic substance. Thus, the study of molecular modifications in chromatin structure and the features of activation and inhibition of various signaling pathways is a pressing task, the resolution of which will enable a deeper understanding of the pathogenesis of diabetes and the development of approaches to correct metabolic disorders.

Metabolic phenotypes reflect patient sex and injury status: A cross-sectional analysis of human synovial fluid

Osteoarthritis is a heterogeneous disease. The objective was to compare differences in underlying cellular mechanisms and endogenous repair pathways between synovial fluid (SF) from male and female participants with different injuries to improve the current understanding of the pathophysiology of downstream post-traumatic osteoarthritis (PTOA). SF from n=33 knee arthroscopy patients between 18 and 70 years with no prior knee injuries was obtained pre-procedure and injury pathology assigned post-procedure. SF was extracted and analyzed via liquid chromatography-mass spectrometry metabolomic profiling to examine differences in metabolism between injury pathologies (ligament, meniscal, and combined ligament and meniscal) and patient sex. Samples were pooled and underwent secondary fragmentation to identify metabolites. Different knee injuries uniquely altered SF metabolites and downstream pathways including amino acid, lipid, and inflammatory-associated metabolic pathways. Notably, sexual dimorphic metabolic phenotypes were examined between males and females and within injury pathology. Cervonyl carnitine and other identified metabolites differed in concentrations between sexes. These results suggest that different injuries and patient sex are associated with distinct metabolic phenotypes. Considering these phenotypic associations, a greater understanding of metabolic mechanisms associated with specific injuries, sex, and PTOA development may yield data regarding how endogenous repair pathways differ between male and female injury types. Ongoing metabolomic analysis of SF in injured male and female patients can be performed to monitor PTOA development and progression.

Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic-associated molecular subtypes and genomic mutations.

Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods: 115 TNBC patients from TCGA were combined into a virtual cohort and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results: In general, TNBC could be classified into two metabolically distinct subtypes. C1 had high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies.

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer.

Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

Metabolic factors associated with the prognosis of oligometastatic patients treated with stereotactic body radiotherapy.

Over the past two decades, it has been established that cancer patients with oligometastases, i.e., only a few detectable metastases confined to one or a few organs, may benefit from an aggressive local treatment approach such as the application of high-precision stereotacticbody radiotherapy (SBRT). Specifically, some studies have indicated that achieving long-term local tumor control of oligometastases is associated with prolonged overall survival. This motivates investigations into which factors may modify the dose-response relationship of SBRT by making metastases more or less radioresistant. One such factor relates to the uptake of the positron emission tomography tracer 2-deoxy-2-[18F]fluoro-D-glucose (FDG) which reflects the extent of tumor cell glycolysis or the Warburg effect, respectively. Here we review the biological mechanisms how the Warburg effect drives tumor cell radioresistance and metastasis and draw connections to clinical studies reporting associations between high FDG uptake and worse clinical outcomes after SBRT for oligometastases. We further review the evidence for distinct metabolic phenotypes of metastases preferentially seeding to specific organs and their possible translation into distinct radioresistance. Finally, evidence that obesity and hyperglycemia also affect outcomes after SBRT will be presented. While delivered dose is the main determinant of a high local tumor control probability, there might be clinical scenarios when metabolic targeting could make the difference between achieving local control or not, for example when doses have to be compromised in order to spare neighboring high-risk organs, or when tumors are expected to be highly therapy-resistant due to heavy pretreatment such as chemotherapy and/or radiotherapy.

Abstract 2047: Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Abstract Background: Glucose and glutamine are major carbon and energy sources that promote the fast proliferation of cancer cells. The Warburg effect, characterized by shifted flux ratio between aerobic and anaerobic respirations, is considered as a common metabolic reprogramming mechanism in cancer. The further discovery of glutaminolysis pathway elucidated the role of glutamate in providing energy and carbon source in cancer metabolism. Variations in the branches of the central metabolism of glucose and glutamate have also been observed. However, most of these observations were made on cell line or mouse systems, which cannot mimic the dysbalanced nutrient, redox, pH and oxygen levels in a real tumor microenvironment (TME), which may heavily shift in a TME and determines the metabolic phenotypes of a cancer. Analysis: In this study, we conducted a systematic evaluation of the metabolic reprogramming and characteristics via a computational analysis by using pan-cancer transcriptomics data of 11 cancer subtypes and 9 matched adjacent normal tissue types. We focused on the flux distribution and variations of the central energy metabolism and its key branches. We first reconstructed the central metabolism pathway in a subcellular resolution by including glycolytic pathway, production of lactate, TCA cycle, nucleic acids synthesis, glutaminolysis, glutaminate, glutamine and glutathione metabolism, and other amino acids synthesis. We applied our in-house developed scFEA method on TCGA pan-cancer transcriptomics data. Results: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of TCA cycle, i.e., Warburg effect in almost all the analyzed cancer types. However, increased lactate production and the second half of TCA cycle were only seen in certain cancer types. More interestingly, we did not see cancer tissues have highly shifted glutaminolysis compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes, (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls, and (3) the different shifts happened to tissue specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors. Citation Format: Alex Lu, Haiqi Zhu, Kevin Hu, Grace Yang, Shaoyang Huang, Pengtao Dang, Sha Cao, Chi Zhang. Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2047.

Loss of Chloroplast GNAT Acetyltransferases Results in Distinct Metabolic Phenotypes in Arabidopsis.

Acetylation is one of the most common chemical modifications found on a variety of molecules ranging from metabolites to proteins. Although numerous chloroplast proteins have been shown to be acetylated, the role of acetylation in the regulation of chloroplast functions has remained mainly enigmatic. The chloroplast acetylation machinery in Arabidopsis thaliana consists of eight General control non-repressible 5 (GCN5)-related N-acetyltransferase (GNAT)-family enzymes that catalyze both N-terminal and lysine acetylation of proteins. Additionally, two plastid GNATs have also been reported to be involved in the biosynthesis of melatonin. Here, we have characterized six plastid GNATs (GNAT1, GNAT2, GNAT4, GNAT6, GNAT7 and GNAT10) using a reverse genetics approach with an emphasis on the metabolomes and photosynthesis of the knock-out plants. Our results reveal the impact of GNAT enzymes on the accumulation of chloroplast-related compounds, such as oxylipins and ascorbate, and the GNAT enzymes also affect the accumulation of amino acids and their derivatives. Specifically, the amount of acetylated arginine and proline was significantly decreased in the gnat2 and gnat7 mutants, respectively, as compared to the wild-type Col-0 plants. Additionally, our results show that the loss of the GNAT enzymes results in increased accumulation of Rubisco and Rubisco activase (RCA) at the thylakoids. Nevertheless, the reallocation of Rubisco and RCA did not have consequent effects on carbon assimilation under the studied conditions. Taken together, our results show that chloroplast GNATs affect diverse aspects of plant metabolism and pave way for future research into the role of protein acetylation.

Metabolic Heterogeneity of Cerebral Cortical and Cerebellar Astrocytes.

Astrocytes play critical roles in regulating neuronal synaptogenesis, maintaining blood-brain barrier integrity, and recycling neurotransmitters. Increasing numbers of studies have suggested astrocyte heterogeneity in morphology, gene profile, and function. However, metabolic phenotype of astrocytes in different brain regions have not been explored. In this paper, we investigated the metabolic signature of cortical and cerebellar astrocytes using primary astrocyte cultures. We observed that cortical astrocytes were larger than cerebellar astrocytes, whereas cerebellar astrocytes had more and longer processes than cortical astrocytes. Using a Seahorse extracellular flux analyzer, we demonstrated that cortical astrocytes had higher mitochondrial respiration and glycolysis than cerebellar astrocytes. Cerebellar astrocytes have lower spare capacity of mitochondrial respiration and glycolysis as compared with cortical astrocytes. Consistently, cortical astrocytes have higher mitochondrial oxidation and glycolysis-derived ATP content than cerebellar astrocytes. In addition, cerebellar astrocytes have a fuel preference for glutamine and fatty acid, whereas cortical astrocytes were more dependent on glucose to meet energy demands. Our study indicated that cortical and cerebellar astrocytes display distinct metabolic phenotypes. Future studies on astrocyte metabolic heterogeneity and brain function in aging and neurodegeneration may lead to better understanding of the role of astrocyte in brain aging and neurodegenerative disorders.