Nuclear Magnetic Resonance technique in tumor metabolism

Recent Advances in Electrochemical Biosensors Targeting Stress Markers

INTRODUCTION

When the body experiences a change in its internal environment due to factors such as mood (euphoria, stress) and illness, it releases biomarkers in large quantities. These biomarkers are used for detecting a disease at its early stages. This involves the detection of insufficient quantities of biocomponents, which can be done by using nanomaterials, conventional materials, and biotechnology; thus, scientists can increase the sensitivity of electrochemical sensors. According to studies conducted in this area, electrochemical sensors have shown promise as a diagnostic tool due to their ability to identify and pinpoint illness biomarkers. The present review article was compiled to gather the latest information on electrochemical biosensors targeting stress markers.

MATERIALS AND METHODS

The authors searched scholarly databases like ScienceDirect, Pubmed, Medline, and Scopus for information on electrochemical biosensors targeting stress markers.

RESULTS

In this article, we looked at the recent developments in electrochemical sensors for stress monitoring. Because of advances in nanomaterial and biomolecule processes, electrochemical biosensors have been developed with the sensitivity to detect several biomarkers in real-time in therapeutically relevant materials.

CONCLUSION

This biomarker sensor strategy can analyze various biofluids (sweat, plasma, urine, and saliva).

Dielectric Spectroscopy Shows a Permittivity Contrast between Meningioma Tissue and Brain White and Gray Matter-A Potential Physical Biomarker for Meningioma Discrimination

The effectiveness of surgical resection of meningioma, the most common primary CNS tumor, depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white and gray matter tissues. Aiming to find a potential biomarker based on tissue permittivity, dielectric spectroscopy of meningioma, white matter, and gray matter ex vivo tissues was performed using the open-ended coaxial probe method in the microwave frequency range from 0.5 to 18 GHz. The averages and the 95% confidence intervals of the measured permittivity for each tissue were compared. The results showed the absence of overlap between the 95% confidence intervals for meningioma tissue and for brain white and gray matter, indicating a significant difference in average permittivity (p ≤ 0.05) throughout almost the entire measured frequency range, with the most pronounced contrast found between 2 GHz and 5 GHz. The discovered contrast is relevant as a potential physical biomarker to discriminate meningioma tissue from the surrounding brain tissues by means of permittivity measurement, e.g., for intraoperative meningioma margin assessment. The permittivity models for each tissue, developed in this study as its byproducts, will allow more accurate electromagnetic modeling of brain tumor and healthy tissues, facilitating the development of new microwave-based medical devices and tools.

Plasma metabolomics by nuclear magnetic resonance reveals biomarkers and metabolic pathways associated with the control of HIV-1 infection/progression

How the human body reacts to the exposure of HIV-1 is an important research goal. Frequently, HIV exposure leads to infection, but some individuals show natural resistance to this infection; they are known as HIV-1-exposed but seronegative (HESN). Others, although infected but without antiretroviral therapy, control HIV-1 replication and progression to AIDS; they are named controllers, maintaining low viral levels and an adequate count of CD4⁺ T lymphocytes. Biological mechanisms explaining these phenomena are not precise. In this context, metabolomics emerges as a method to find metabolites in response to pathophysiological stimuli, which can help to establish mechanisms of natural resistance to HIV-1 infection and its progression. We conducted a cross-sectional study including 30 HESN, 14 HIV-1 progressors, 14 controllers and 30 healthy controls. Plasma samples (directly and deproteinized) were analyzed through Nuclear Magnetic Resonance (NMR) metabolomics to find biomarkers and altered metabolic pathways. The metabolic profile analysis of progressors, controllers and HESN demonstrated significant differences with healthy controls when a discriminant analysis (PLS-DA) was applied. In the discriminant models, 13 metabolites associated with HESN, 14 with progressors and 12 with controllers were identified, which presented statistically significant mean differences with healthy controls. In progressors, the metabolites were related to high energy expenditure (creatinine), mood disorders (tyrosine) and immune activation (lipoproteins), phenomena typical of the natural course of the infection. In controllers, they were related to an inflammation-modulating profile (glutamate and pyruvate) and a better adaptive immune system response (acetate) associated with resistance to progression. In the HESN group, with anti-inflammatory (lactate and phosphocholine) and virucidal (lactate) effects which constitute a protective profile in the sexual transmission of HIV. Concerning the significant metabolites of each group, we identified 24 genes involved in HIV-1 replication or virus proteins that were all altered in progressors but only partially in controllers and HESN. In summary, our results indicate that exposure to HIV-1 in HESN, as well as infection in progressors and controllers, affects the metabolism of individuals and that this affectation can be determined using NMR metabolomics.

The Relationship between Histological Composition and Metabolic Profile in Breast Tumors and Peritumoral Tissue Determined with 1H HR-MAS NMR Spectroscopy

Breast tumors constitute the complex entities composed of cancer cells and stromal components. The compositional heterogeneity should be taken into account in bulk tissue metabolomics studies. The aim of this work was to find the relation between the histological content and ¹H HR-MAS (high-resolution magic angle spinning nuclear magnetic resonance) metabolic profiles of the tissue samples excised from the breast tumors and the peritumoral areas in 39 patients diagnosed with invasive breast carcinoma. The total number of the histologically verified specimens was 140. The classification accuracy of the OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) model differentiating the cancerous from non-involved samples was 87% (sensitivity of 72.2%, specificity of 92.3%). The metabolic contents of the epithelial and stromal compartments were determined from a linear regression analysis of the levels of the evaluated compounds against the cancer cell fraction in 39 samples composed mainly of cancer cells and intratumoral fibrosis. The correlation coefficients between the levels of several metabolites and a tumor purity were found to be dependent on the tumor grade (I vs II/III). The comparison of the levels of the metabolites in the intratumoral fibrosis (obtained from the extrapolation of the regression lines to 0% cancer content) to those levels in the fibrous connective tissue beyond the tumors revealed a profound metabolic reprogramming in the former tissue. The joint analysis of the metabolic profiles of the stromal and epithelial compartments in the breast tumors contributes to the increased understanding of breast cancer biology.

Review on Metal-Based Theranostic Nanoparticles for Cancer Therapy and Imaging

Theranostic agents are promising due to their ability to diagnose, treat and monitor different types of cancer using a variety of imaging modalities. The advantage specifically of nanoparticles is that they can accumulate easily at the tumor site due to the large gaps in blood vessels near tumors. Such high concentration of theranostic agents at the target site can lead to enhancement in both imaging and therapy. This article provides an overview of nanoparticles that have been used for cancer theranostics, and the different imaging, treatment options and signaling pathways that are important when using nanoparticles for cancer theranostics. In particular, nanoparticles made of metal elements are emphasized due to their wide applications in cancer theranostics. One important aspect discussed is the ability to combine different types of metals in one nanoplatform for use as multimodal imaging and therapeutic agents for cancer.

Advances in nano- and microscale NMR spectroscopy using diamond quantum sensors

Quantum technologies have seen a rapid developmental surge over the last couple of years. Though often overshadowed by quantum computation, quantum sensors show tremendous potential for widespread applications in chemistry and biology. One system stands out in particular: the nitrogen-vacancy (NV) center in diamond, an atomic-sized sensor allowing the detection of nuclear magnetic resonance (NMR) signals at unprecedented length scales down to a single proton. In this article, we review the fundamentals of NV center-based quantum sensing and its distinct impact on nano- and microscale NMR spectroscopy. Furthermore, we highlight possible future applications of this novel technology ranging from energy research, materials science, to single-cell biology, and discuss the associated challenges of these rapidly developing NMR sensors.

Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy

Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm $$\end{document}± 0.03% and a specificity of 95.44 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm $$\end{document}± 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting.

A Metabolomic Investigation of Eugenol on Colorectal Cancer Cell Line HT-29 by Modifying the Expression of APC, p53, and KRAS Genes

Colorectal cancer is one of the most lethal cancers with a high mortality rate. Chemotherapy results in drug resistance in some cases; hence, herbal medicines are sometimes used in adjunct with it. Eugenol has been reported to have anti-inflammatory, antioxidant, and anticancer properties. Metabolomics is a study of metabolic changes within an organism using high-throughput technology. The purpose of this research was to investigate the anticancer effects of eugenol and variations in p53, KRAS, and APC gene expression and metabolic changes associated with the abovementioned gene expressions using ¹HNMR spectroscopy. The MTT method was used to determine cell viability and its IC50 detected. After treating HT-29 cells with IC50 concentration of eugenol, RNA was extracted and cDNA was obtained from them and the expression of p53, KRAS, and APC genes was measured using the qRT-PCR technique. Metabolites were extracted using the chloroform-ethanol method, lyophilized, and sent for ¹HNMR spectroscopy using the 1D-NOESY protocol. Chemometrics analysis such as PLS-DA was performed, and differentiated metabolites were identified using the Human Metabolome Database. Integrated metabolic analysis using the metabolites and gene expression was performed by the MetaboAnalyst website. The observed IC50 for eugenol was 500 μM, and the relative expression of APC and p53 genes in the treated cells increased compared to the control group, and the expression of KRAS oncogene gene decreased significantly. The crucial changes in convergent metabolic phenotype with genes were identified. The results indicate that eugenol exhibits its antitumor properties by targeting a specific biochemical pathway in the cell's metabolome profile due to changes in genes involved in colon cancer.

An integrative approach based on GC-qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkers

INTRODUCTION

Globally, breast cancer (BC) is leading at the top of women's diseases and, as a multifactorial disease, there is the need for the development of new approaches to aid clinicians on monitoring BC treatments. In this sense, metabolomic studies have become an essential tool allowing the establishment of interdependency among metabolites in biological samples.

OBJECTIVE

The combination of nuclear magnetic resonance (NMR) and gas chromatography-quadrupole mass spectrometry (GC-qMS) based metabolomic analyses of urine and breast tissue samples from BC patients and cancer-free individuals was used.

METHODS

Multivariate statistical tools were used in order to obtain a panel of metabolites that could discriminate malignant from healthy status assisting in the diagnostic field. Urine samples (n = 30), cancer tissues (n = 30) were collected from BC patients, cancer-free tissues were resected outside the tumor margin from the same donors (n = 30) while cancer-free urine samples (n = 40) where obtained from healthy subjects and analysed by NMR and GC-qMS methodologies.

RESULTS

The orthogonal partial least square discriminant analysis model showed a clear separation between BC patients and cancer-free subjects for both classes of samples. Specifically, for urine samples, the goodness of fit (R²Y) and predictive ability (Q²) was 0.946 and 0.910, respectively, whereas for tissue was 0.888 and 0.813, revealing a good predictable accuracy. The discrimination efficiency and accuracy of tissue and urine metabolites was ascertained by receiver operating characteristic curve analysis that allowed the identification of metabolites with high sensitivity and specificity. The metabolomic pathway analysis identified several dysregulated pathways in BC, including those related with lactate, valine, aspartate and glutamine metabolism. Additionally, correlations between urine and tissue metabolites were investigated and five metabolites (e.g. acetone, 3-hexanone, 4-heptanone, 2-methyl-5-(methylthio)-furan and acetate) were found to be significant using a dual platform approach.

CONCLUSION

Overall, this study suggests that an improved metabolic profile combining NMR and GC-qMS may be useful to achieve more insights regarding the mechanisms underlying cancer.

Aromatic amino acids play a harmonizing role in prostate cancer: A metabolomics-based cross-sectional study

BACKGROUND

Prostate cancer (PCa) is a common health problem worldwide. The rate of this disease is likely to grow by 2021. PCa is a heterogeneous disorder, and various biochemical factors contribute to the development of this disease. The metabolome is the complete set of metabolites in a cell or biological sample and represents the downstream end product of the omics. Hence, to model PCa by computational systems biology, a preliminary metabolomics-based study was used to compare the metabolome profile pattern between healthy and PCa men.

OBJECTIVE

This study was carried out to highlight energy metabolism modification and assist the prognosis and treatment of disease with unique biomarkers.

MATERIALS AND METHODS

In this cross-sectional research, 26 men diagnosed with stage-III PCa and 26 healthy men with normal PSA levels were enrolled. Urine was analyzed with proton nuclear magnetic resonance (1 H-NMR) spectroscopy, accompanied by the MetaboAnalyst web-based platform tool for metabolomics data analysis. Partial least squares regression discriminant analysis was applied to clarify the separation between the two groups. Outliers were documented and metabolites determined, followed by identifying biochemical pathways.

RESULTS

Our findings reveal that modifications in aromatic amino acid metabolism and some of their metabolites have a high potential for use as urinary PCa biomarkers. Tryptophan metabolism (p < 0.001), tyrosine metabolism (p < 0.001), phenylalanine, tyrosine and tryptophan biosynthesis (p < 0.001), phenylalanine metabolism (p = 0.01), ubiquinone and other terpenoid-quinone biosynthesis (p = 0.19), nitrogen metabolism (p = 0.21), and thiamine metabolism (p = 0.41) with Q2 (0.198) and R2 (0.583) were significantly altered.

CONCLUSION

The discriminated metabolites and their pathways play an essential role in PCa causes and harmony.

Clinical applications of breast cancer metabolomics using high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS): systematic scoping review

INTRODUCTION

Breast cancer is a heterogeneous disease with different prognoses and responses to systemic treatment depending on its molecular characteristics, which makes it imperative to develop new biomarkers for an individualized diagnosis and personalized oncological treatment. Ex vivo high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS) is the most common technique for metabolic quantification in human surgical and biopsy tissue specimens.

OBJECTIVE

To perform a review of the current available literature on the clinical applications of HRMAS 1H MRS metabolic analysis in tissue samples of breast cancer patients.

METHODS

This systematic scoping review included original research papers published in the English language in peer-reviewed journals. Study selection was performed independently by two reviewers and preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were followed.

RESULTS

The literature search returned 159 studies and 26 papers were included as part of this systematic review. There was considerable variation regarding tissue type, aims, and statistical analysis methods across the different studies. To facilitate the interpretation of the results, the included studies were grouped according to their aims or main outcomes into: feasibility and tumor diagnosis (n = 6); tumor heterogeneity (n = 2); correlation with proteomics/transcriptomics (n = 3); correlation with prognostic factors (n = 11); and response evaluation to NAC (n = 4).

CONCLUSION

There is a lot of potential in including metabolic information of breast cancer tissue obtained with HRMAS 1H MRS. To date, studies show that metabolic concentrations quantified by this technique can be related to the diagnosis, prognosis, and treatment response in breast cancer patients.

Epigenetics and Metabolism in Health and Disease

In the next 10 years, one billion people are estimated to suffer from disabling consequences of metabolic disorders, making them the number one noncommunicable disease on a global scale by 2030. Lots of risk factors such as dietary intake, lack of exercise and other life style behaviors are considered to play a role in the development of metabolic disorders. Despite the efforts that have been undertaken to unravel their potential causes, the underlying molecular mechanisms remain elusive. Evidence suggests that the pathogenesis involves changes on chromatin and chromatin-modifying enzymes, which can contribute to a persistent dysregulated metabolic phenotype. Indeed, a rising number of studies links epigenetic alterations with the diagnosis and prognosis of metabolic disorders. A prerequisite for exploiting these findings for pharmacological intervention is a detailed understanding of how differential epigenetic modifications control cell metabolism. In this mini review, we summarize the recent advances in uncovering the interplay between epigenetics and metabolic pathways on a cellular level and highlight potential new avenues for alternative treatment strategies.