Unveiling metabolic changes in marsupialized odontogenic keratocyst: A pilot study

L-cysteine contributes to destructive activities of odontogenic cysts/tumor

BACKGROUND

Odontogenic cysts/tumor can cause severe bone destruction, which affects maxillofacial function and aesthetics. Meanwhile, metabolic reprogramming is an important hallmark of diseases. Changes in metabolic flow affect all aspects of disease, especially bone-related diseases. At present, the researches on pathogenesis of odontogenic cysts/tumor are mainly focused on the level of gene regulation, but the effects of metabolic alterations on odontogenic cysts/tumor have still underexplored.

MATERIALS AND METHODS

Imaging analysis was used to evaluate the lesion size of different odontogenic lesions. Tartrate resistant acid phosphatase (TRAP) and immunohistochemistry (IHC) assays were utilized to detect the differences in bone destruction activity in odontogenic cysts and tumors. Furthermore, metabolomics and weighted gene co-expression network analysis (WGCNA) were conducted for the metabolomic features and key metabolite screening, respectively. The effect of ferroptosis inhibition on bone destruction was confirmed by IHC, immunofluorescence, and malondialdehyde colorimetric assay.

RESULTS

The bone destruction activity of ameloblastoma (AM) was the strongest and the weakest in odontogenic cysts (OC). High-throughput targeted metabolomics was used to map the metabolomic profiles of OC, odontogenic keratocyst (OKC) and AM. WGCNA and differential analysis identified L-cysteine in OKC and AM. Cystathionine γ-lyase (CTH) was further screened by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The functions of L-cysteine were further validated. Finally, we confirmed that CTH affected destructive activities by regulating the sensitivity of epithelial cells to ferroptosis.

CONCLUSION

High-throughput targeted metabolomics performed on diseased tissue confirmed the unique alteration of metabolic profiles in OKC and AM. CTH and its metabolite L-cysteine are the key factors regulating destructive activities.

Discovery of metabolite biomarkers for odontogenic keratocysts

INTRODUCTION

Odontogenic keratocysts (OKCs) are locally aggressive and have a high rate of recurrence, but the pathogenesis of OKCs is not fully understood. We aimed to investigate the serum metabolomic profile of OKCs and discover potential biomarkers.

METHODS

Metabolomic analysis was performed on 42 serum samples from 22 OKC patients and 20 healthy controls (HCs) using gas chromatography‒mass spectrometry to identify dysregulated metabolites in the OKC samples. LASSO regression and receiver operating characteristic (ROC) curve analyses were used to select and validate metabolic biomarkers and develop diagnostic models.

RESULTS

A total of 73 metabolites were identified in the serum samples, and 24 metabolites were dysregulated in the OKC samples, of which 4 were upregulated. Finally, a diagnostic panel of 10 metabolites was constructed that accurately diagnosed OKCs (sensitivity of 100%, specificity of 100%, area under the curve of 1.00).

CONCLUSION

This study is the first to investigate the metabolic characteristics and potential metabolic biomarkers in the serum of OKC patients using GC‒MS. Our study provides further evidence to explore the pathogenesis of OKC.

Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study

BACKGROUND AND AIMS

Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform.

METHODS

GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples.

RESULTS

The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh).

CONCLUSION

This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.

Integrated proteomics, phosphoproteomics and metabolomics analyses reveal similarities amongst giant cell granulomas of the jaws with different genetic mutations

BACKGROUND

Giant cell granuloma of the jaws are benign osteolytic lesions of the jaws. These lesions are genetically characterized by mutually exclusive somatic mutations at TRPV4, KRAS, and FGFR1, and a fourth molecular subgroup which is wild-type for the three mutations. Irrespective of the molecular background, giant cell granulomas show MAPK/ERK activation. However, it remains unclear if these mutations lead to differences in their molecular signaling in giant cell granulomas.

METHODS

Metabolomics, proteomics and phosphoproteomics analyses were carried out in formalin-fixed paraffin-embedded samples of giant cell granuloma of the jaws. The study cohort consisted of five lesions harboring mutations in FGFR1, six in KRAS, five in TRPV4 and five that were wild-type for these mutations.

RESULTS

Lesions harboring KRAS or FGFR1 mutations showed overall similar proteomics and metabolomics profiles. In all four groups, metabolic pathways showed similarity in apoptosis, cell signaling, gene expression, cell differentiation and erythrocyte activity. Lesions harboring TRPV4 mutations showed a greater number of enriched pathways related to tissue architecture. On the other hand, the wild-type group presented increased number of enriched pathways related to protein metabolism compared to the other groups.

CONCLUSION

Despite some minor differences, our results revealed an overall similar molecular profile among the groups with different mutational profile at the metabolic, proteic and phosphopeptidic levels. This article is protected by copyright. All rights reserved.