Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer

Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

A metabolomics study on carcinogenesis of ground-glass nodules

Objective

This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer.

Material and Methods

Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure.

Results

Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples.

Conclusion

Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.

Studies on the Changes of Fermentation Metabolites and the Protective Effect of Fermented Edible Grass on Stress Injury Induced by Acetaminophen in HepG2 Cells

In this study, gas chromatography-mass spectrometry (GC-MS) based untargeted metabolomics was used to describe the changes of metabolites in edible grass with Lactobacillus plantarum (Lp) fermentation durations of 0 and 7 days, and subsequently to investigate the protective effect of fermented edible grass on acetaminophen-induced stress injury in HepG2 cells. Results showed that 53 differential metabolites were identified, including 31 significantly increased and 22 significantly decreased metabolites in fermented edible grass. Fermented edible grass protected HepG2 cells against acetaminophen-induced stress injury, which profited from the reduction in lactate dehydrogenase (LDH) and malondialdehyde (MDA) levels and the enhancement in superoxide dismutase (SOD) activity. Cell metabolomics analysis revealed that a total of 13 intracellular and 20 extracellular differential metabolites were detected. Fermented edible grass could regulate multiple cell metabolic pathways to exhibit protective effects on HepG2 cells. These findings provided theoretical guidance for the formation and regulation of bioactive metabolites in fermented edible grass and preliminarily confirmed the protective effects of fermented edible grass on drug-induced liver damage.

Hyperpolarization-Enhanced NMR Spectroscopy of Unaltered Biofluids Using Photo-CIDNP

The direct and unambiguous detection and identification of individual metabolite molecules present in complex biological mixtures constitute a major challenge in (bio)analytical research. In this context, nuclear magnetic resonance (NMR) spectroscopy has proven to be particularly powerful owing to its ability to provide both qualitative and quantitative atomic-level information on multiple analytes simultaneously in a noninvasive manner. Nevertheless, NMR suffers from a low inherent sensitivity and, moreover, lacks selectivity regarding the number of individual analytes to be studied in a mixture of a myriad of structurally and chemically very different molecules, e.g., metabolites in a biofluid. Here, we describe a method that circumvents these shortcomings via performing selective, photochemically induced dynamic nuclear polarization (photo-CIDNP) enhanced NMR spectroscopy on unmodified complex biological mixtures, i.e., human urine and serum, which yields a single, background-free one-dimensional NMR spectrum. In doing this, we demonstrate that photo-CIDNP experiments on unmodified complex mixtures of biological origin are feasible, can be performed straightforwardly in the native aqueous medium at physiological metabolite concentrations, and act as a spectral filter, facilitating the analysis of NMR spectra of complex biofluids. Due to its noninvasive nature, the method is fully compatible with state-of-the-art metabolomic protocols providing direct spectroscopic information on a small, carefully selected subset of clinically relevant metabolites. We anticipate that this approach, which, in addition, can be combined with existing high-throughput/high-sensitivity NMR methodology, holds great promise for further in-depth studies and development for use in metabolomics and many other areas of analytical research.

Proteomics and Metabolomics in Biomedicine

The technological advances of recent years have significantly enhanced medical discoveries [...].

Ultrahigh-performance liquid chromatography-high-resolution mass spectrometry-based plasma metabolomics study of thymoma and thymic hyperplasia

RATIONALE

Thymoma is a rare malignant tumor but it is the most common primary tumor of the anterior mediastinum. The current imaging methods for thymoma screening suffer from false positive rate problems, and thymoma pathogenesis remains elusive. Study of thymoma metabolic characteristics could provide clues for improving the diagnosis and understanding the pathogenesis of thymoma.

METHODS

Metabolic profiling of plasma from thymoma and thymic hyperplasia patients was performed using ultrahigh-performance liquid chromatography combined with high-resolution mass spectrometry in both positive and negative ionization modes. After pre- and post-processing, the dataset was divided into three age groups and statistical analysis was performed to select differential metabolites of thymoma. For feature identification, experimental tandem mass spectra were matched to those of databases and available chemical standards, and also manually annotated with plausible chemical structures to ensure high identification confidence.

RESULTS

A total of 47 differential metabolites were identified in thymoma. Significantly higher levels of histidine, sphinganine 1-phosphate, lactic acid dimer, phenylacetylglutamine, LPC (18:3) and LPC (16:1), and significantly lower levels of phenylalanine, indole-3-propionic acid (IPA), hippuric acid and mesobilirubinogen were associated with thymoma. Tryptophan level in thymoma-associated myasthenia gravis (TAMG) was significantly lower than that of the MG(-) group. IPA and hippuric acid abundances exhibited increasing trends from indolent to aggressive thymoma.

CONCLUSIONS

Our study revealed aberrant aromatic amino acid metabolism and fatty acid oxidation might be associated with thymoma. The identified unique metabolic characteristics of thymoma may provide valuable information for study of the molecular mechanism of thymoma pathogenesis, and improvement of diagnosis and discovery of new therapeutic strategies for thymoma.

The changes in metabolomics profile induced by intermittent theta burst stimulation in major depressive disorder: an exploratory study

BACKGROUND

Recently, there has been an ongoing interest in the mechanism of intermittent theta burst stimulation (iTBS) in major depressive disorder. Studying the metabolite changes induced by iTBS may help to understand the mechanism.

METHODS

Eleven participants with major depressive disorder received 10 days iTBS treatment. Magnetic resonance imaging (MRI) was used to target the region of the left dorsolateral prefrontal cortex (DLPFC) in each participant. We analyzed the effects of iTBS on metabolites using high-throughput profiling and assessed its impact on depressive symptoms. These analyses were considered exploratory, and no correction for multiple comparisons was applied.

RESULTS

Among the 318 measured metabolites, a significant increase in cystine, asymmetric dimethylarginine (ADMA), 1-methylhistidine, indoleacetic acid (IAA), diethanolamine (DEA), dopa, riboflavin-5'-monophosphate (FMN), and a significant decrease in alphalinolenic acid (ALA), gamma-linolenic acid (GLA), serotonin, linoleic acid (LA) (p < 0.05) were detected in the patients after iTBS treatment. In Pearson correlation analysis, the plasma levels of LA, FMN and ADMA at baseline were significantly related to the reduction rate of the 17-item Hamilton Depression Rating Scale and the Patient Health Questionnaire-9 scores (p < 0.05).

CONCLUSIONS

Our study highlights that LA, FMN, ADMA and their relationship with oxidative stress, may be key factors in the antidepressant efficacy of iTBS.

The combined analysis of urine and blood metabolomics profiles provides an accurate prediction of the training and competitive status of Chinese professional swimmers

Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cis-aconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793-0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888-0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers.

Benchtop NMR-Based Metabolomics: First Steps for Biomedical Application

Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.

Metabolomics in oncology

BACKGROUND

Oncogenic transformation alters intracellular metabolism and contributes to the growth of malignant cells. Metabolomics, or the study of small molecules, can reveal insight about cancer progression that other biomarker studies cannot. Number of metabolites involved in this process have been in spotlight for cancer detection, monitoring, and therapy.

RECENT FINDINGS

In this review, the "Metabolomics" is defined in terms of current technology having both clinical and translational applications. Researchers have shown metabolomics can be used to discern metabolic indicators non-invasively using different analytical methods like positron emission tomography, magnetic resonance spectroscopic imaging etc. Metabolomic profiling is a powerful and technically feasible way to track changes in tumor metabolism and gauge treatment response across time. Recent studies have shown metabolomics can also predict individual metabolic changes in response to cancer treatment, measure medication efficacy, and monitor drug resistance. Its significance in cancer development and treatment is summarized in this review.

CONCLUSION

Although in infancy, metabolomics can be used to identify treatment options and/or predict responsiveness to cancer treatments. Technical challenges like database management, cost and methodical knowhow still persist. Overcoming these challenges in near further can help in designing new treatment régimes with increased sensitivity and specificity.

Disentangling the Complexity of Nutrition, Frailty and Gut Microbial Pathways during Aging: A Focus on Hippuric Acid

Hippuric acid (HA) is a metabolite resulting from the hepatic glycine conjugation of benzoic acid (BA) or from the gut bacterial metabolism of phenylalanine. BA is generally produced by gut microbial metabolic pathways after the ingestion of foods of vegetal origin rich in polyphenolic compounds, namely, chlorogenic acids or epicatechins. It can also be present in foods, either naturally or artificially added as a preservative. The plasma and urine HA levels have been used in nutritional research for estimating the habitual fruit and vegetable intake, especially in children and in patients with metabolic diseases. HA has also been proposed as a biomarker of aging, since its levels in the plasma and urine can be influenced by the presence of several age-related conditions, including frailty, sarcopenia and cognitive impairment. Subjects with physical frailty generally exhibit reduced plasma and urine levels of HA, despite the fact that HA excretion tends to increase with aging. Conversely, subjects with chronic kidney disease exhibit reduced HA clearance, with HA retention that may exert toxic effects on the circulation, brain and kidneys. With regard to older patients with frailty and multimorbidity, interpreting the HA levels in the plasma and urine may result particularly challenging because HA is at the crossroads between diet, gut microbiota, liver and kidney function. Although these considerations may not make HA the ideal biomarker of aging trajectories, the study of its metabolism and clearance in older subjects may provide valuable information for disentangling the complex interaction between diet, gut microbiota, frailty and multimorbidity.