Quantification of succinic acid levels, linked to succinate dehydrogenase (SDH) dysfunctions, by an automated and fully validated liquid chromatography tandem mass spectrometry method suitable for multi-matrix applications

Detection, mechanisms, and therapeutic implications of oncometabolites

Metabolic abnormalities are a hallmark of cancer cells and are essential to tumor progression. Oncometabolites have pleiotropic effects on cancer biology and affect a plethora of processes, from oncogenesis and metabolism to therapeutic resistance. Targeting oncometabolites, therefore, could offer promising therapeutic avenues against tumor growth and resistance to treatments. Recent advances in characterizing the metabolic profiles of cancer cells are shedding light on the underlying mechanisms and associated metabolic networks. This review summarizes the diverse detection methods, molecular mechanisms, and therapeutic targets of oncometabolites, which may lead to targeting oncometabolism for cancer therapy.

A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients

Colorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700-0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94-1).

Discovery of anti-allergic components in Guomingkang Formula using sensitive HEMT biochips coupled with in vitro and in vivo validation

BACKGROUND

Allergic rhinitis (AR) is a prevalent allergic disease, which seriously affects the sufferers' life quality and increases the socioeconomic burden. Guominkang (GMK), a well-known prescription for AR treatment, showed satisfactory effects; while its anti-allergic components remain to be disclosed. AlGaN/GaN HEMT biochip is more sensitive and cost-effective than other binding equipments, indicating its great potential for screening of active ingredients from herbal medicines.

METHODS

AR mouse models were first established to test the anti-allergic effect of GMK and discover the ingredients absorbed into blood by ultra-high performance liquid chromatography-mass spectra (UHPLC-MS). Then, novel Syk/Lyn/Fyn-functionalized high electron mobility transistor (HEMT) biochips with high sensitivity and specificity were constructed and applied to screen the active components. Finally, the results from HEMT biochips screening were validated via in silico (molecular docking and molecular dynamics simulation), in vitro (RBL-2H3 cells), and in vivo (PCA mice model) assays.

RESULTS

GMK showed a potent therapeutic effect on AR mice, and fifteen components were identified from the medicated plasma. Furthermore, hamaudol was firstly found to selectively inhibit the Syk and Lyn, and emodin was to selectively inhibit Lyn, which were further confirmed by isothermal titration calorimetry, molecular docking, and molecular dynamics simulation analyses. Suppression of the activation of FcεRI-MAPK signals might be the possible mechanism of the anti-allergic effect of hamaudol.

CONCLUSIONS

The targets of emodin and hamaudol were discovered by HEMT biochips for the first time. This study provided a novel and effective strategy to discover active components in a complex herbal formula by using AlGaN/GaN HEMT biochips.

Succinate: A Serum Biomarker of SDHB-Mutated Paragangliomas and Pheochromocytomas

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) are rare neuroendocrine tumors that are frequently associated with succinate dehydrogenase (SDH) germline mutations. When mutated, SDH losses its function, thus leading to succinate accumulation.

OBJECTIVE

In this study, we evaluated serum succinate levels as a new metabolic biomarker in SDHx-related carriers.

METHODS

Retrospective monocentric study of 88 PPGL patients (43 sporadic, 35 SDHB, 10 SDHA/C/D), 17 tumor-free familial asymptomatic carriers (13 SDHB, 4 SDHC/D), and 60 healthy controls. Clinical, biological, and imaging data were reviewed. Serum succinate levels (n = 280) were quantified by an ultra-performance liquid chromatography coupled to a tandem mass spectrometry method and correlated to SDHx mutational status, disease extension, and other biological biomarkers.

RESULTS

Serum succinate levels > 7 μM allowed identification of tumor-free asymptomatic SDHB-mutated cases compared to a healthy control group (100% specificity; 85% sensitivity). At PPGL diagnosis, SDHB-mutated patients had a significantly increased median succinate level (14 μM) compared to sporadic patients (8 μM) (P < 0.01). Metastatic disease extension was correlated to serum succinate levels (r = 0.81). In the SDHB group, patients displaying highest tumor burdens showed significant increased succinate levels compared to the sporadic group (P < 0.0001).

CONCLUSIONS

In this pilot study, we showed that serum succinate level is an oncometabolic biomarker that should be useful to identify SDHB-related carriers. Succinate levels are also a marker of metabolic tumor burden in patients with a metastatic PPGL and a potential marker of treatment response and follow-up.