Metabolic and lipidomic characterization of radioresistant MDA-MB-231 human breast cancer cells to investigate potential therapeutic targets

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

S-Allyl-L-Cysteine Affects Cell Proliferation and Expression of H2S-Synthetizing Enzymes in MCF-7 and MDA-MB-231 Adenocarcinoma Cell Lines

S-allyl-L-cysteine (SAC) is a sulfur compound present in fresh garlic. The reference literature describes its anticancer, antioxidant and neuroprotective effects. Breast cancer is infamously known as one of the most commonly diagnosed malignancies among women worldwide. Its morbidity and mortality make it reasonable to complete and expand knowledge on this cancer's characteristics. Hydrogen sulfide (H2S) and its naturally occurring donors are well-known investigation subjects for diverse therapeutic purposes. This study was conducted to investigate the SAC antiproliferative potential and effect on three enzymes involved in H2S metabolism: 3-mercaptopyruvate sulfurtransferase (MPST), cystathionine γ-lyase (CTH), and cystathionine β-synthase (CBS). We chose the in vitro cellular model of human breast adenocarcinomas: MCF-7 and MDA-MB-231. The expression of enzymes after 2, 4, 6, 8, and 24 h incubation with 2.24 mM, 3.37 mM, and 4.50 mM SAC concentrations was examined. The number of living cells was determined by the MTS assay. Changes in cellular plasma membrane integrity were measured by the LDH test. Expression changes at the protein level were analyzed using Western blot. A significant decrease in viable cells was registered for MCF-7 cells after all incubation times upon 4.50 mM SAC exposure, and after 6 and 24 h only in MDA-MB-231 upon 4.50 mM SAC. In both cell lines, the MPST gene expression significantly increased after the 24 h incubation with 4.50 mM SAC. S-allyl-L-cysteine had opposite effects on changes in CTH and CBS expression in both cell lines. In our research model, we confirmed the antiproliferative potential of SAC and concluded that our studies provided current information about the increase in MPST gene expression mediated by S-allyl-L-cysteine in the adenocarcinoma in vitro cellular model for the MCF-7 and MDA-MB-231 cell lines. Further investigation of this in vitro model can bring useful information regarding sulfur enzyme metabolism of breast adenocarcinoma and regulating its activity and expression (gene silencing) in anticancer therapy.

Noninvasive Red Laser Intervention before Radiotherapy of Triple-negative Breast Cancer in a Murine Model

Radiotherapy is a well-established cancer treatment; it is estimated that approximately 52% of oncology patients will require this treatment modality at least once. However, some tumors, such as triple-negative breast cancer (TNBC), may present as radioresistant and thus require high doses of ionizing radiation and a prolonged period of treatment, which may result in more severe side effects. Moreover, such tumors show a high incidence of metastases and decreased survival expectancy of the patient. Thus, new strategies for radiosensitizing TNBC are urgently needed. Red light therapy, photobiomodulation, has been used in clinical practice to mitigate the adverse side effects usually associated with radiotherapy. However, no studies have explored its use as a radiosensitizer of TNBC. Here, we used TNBC-bearing mice as a radioresistant cancer model. Red light treatment was applied in three different protocols before a high dose of radiation (60 Gy split in 4 fractions) was administered. We evaluated tumor growth, mouse clinical signs, total blood cell counts, lung metastasis, survival, and levels of glutathione in the blood. Our data showed that the highest laser dose in combination with radiation arrested tumor progression, likely due to inhibition of GSH synthesis. In addition, red light treatment before each fraction of radiation, regardless of the light dose, improved the health status of the animals, prevented anemia, reduced metastases, and improved survival. Collectively, these results indicate that red light treatment in combination with radiation could prove useful in the treatment of TNBC.

The role of lipid metabolism in cancer radioresistance

Radiotherapy is one of the main therapies for cancer. The process leading to radioresistance is still not fully understood. Cancer radiosensitivity is related to the DNA reparation of cancer cells and the tumor microenvironment (TME), which supports cancer cell survival. Factors that affect DNA reparation and the TME can directly or indirectly affect the radiosensitivity of cancer. Recent studies have shown that lipid metabolism in cancer cells, which is involved in the stability of cell membrane structure, energy supply and signal transduction of cancer cells, can also affect the phenotype and function of immune cells and stromal cells in the TME. In this review, we discussed the effects of lipid metabolism on the radiobiological characteristics of cancer cells and the TME. We also summarized recent advances in targeted lipid metabolism as a radiosensitizer and discussed how these scientific findings could be translated into clinical practice to improve the radiosensitivity of cancer.

Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

Structural characterization and bioimaging of Zn2+ using meta-benziporphodimethene analogue

"Prevention is better than cure, especially when something has no cure." Cancer, in most patients is detected at the stage beyond which it becomes non-curative. Thus, the early detection of cancer cells can play a crucial role in enhancing the chances of a patient's survival. In this light, we present a non-fluorescent receptor employed for the detection of Zn²⁺ ion in MDA-MB-231 carcinoma cells that exhibits fluorescence turn-on behaviour upon binding with the metal ion. In this work, the synthesis of 11,16-bis(2,6-difluorobenzene)-6,6,21,21-tetramethyl-meta-benziporpho-6,21-dimethene and its Zn²⁺ chloride complex have been reported. The compounds were fully characterized using UV-Visible, NMR, IR and mass spectrometry. Furthermore, the X-ray polymorphs of meta-benziporphodimethene analogue have been added. The study of its bioimaging applications in MDA-MB-231 breast cancer cells for the detection of Zn²⁺ ions have been reported.

Direct analysis in real time high-resolution mass spectrometry for authenticity assessment of lamb

In comparison to more traditional methods of determining food authenticity, such as gas chromatography analysis, the primary advantages of DART-HRMS include its high speed and throughput of analysis. This study used a non-targeted metabolomics method based on real-time high-resolution mass spectrometry combined with chemometric analysis to distinguish lamb samples from four regions. Orthogonal least squares-discriminant analysis revealed a distinct difference between these four lamb regions. The potential markers were chosen based on the variable's importance in projection values, variance, and fold change. A total of 79 markers were identified using the matching chemistry database. These markers differed significantly between lambs in four regions according to heatmap analysis. The linear discriminatory analysis model had an initial classification rate of 100.0% and a cross-validation accuracy of 82.50% on the identified markers. The research demonstrates that DART-HRMS can perform a rapid authentication evaluation of lamb samples.