Potential metabolomic biomarkers for evaluation of adriamycin efficacy using a urinary 1H-NMR spectroscopy

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Alterations in the gastric microbiota and metabolites in gastric cancer: An update review

Gastric cancer (GC) is one of the leading causes of cancer mortality worldwide. Numerous studies have shown that the gastric microbiota can contribute to the occurrence and development of GC by generating harmful microbial metabolites, suggesting the possibility of discovering biomarkers. Metabolomics has emerged as an advanced promising analytical method for the analysis of microbiota-derived metabolites, which have greatly accelerated our understanding of host-microbiota metabolic interactions in GC. In this review, we briefly compiled recent research progress on the changes of gastric microbiota and its metabolites associated with GC. And we further explored the application of metabolomics and gastric microbiome association analysis in the diagnosis, prevention and treatment of GC.

Balancing the Equation: A Natural History of Trimethylamine and Trimethylamine-N-oxide

Trimethylamine (TMA) and its N-oxide (TMAO) are ubiquitous in prokaryote and eukaryote organisms as well as in the environment, reflecting their fundamental importance in evolutionary biology, and their diverse biochemical functions. Both metabolites have multiple biological roles including cell-signaling. Much attention has focused on the significance of serum and urinary TMAO in cardiovascular disease risk, yet this is only one of the many facets of a deeper TMA-TMAO partnership that reflects the significance of these metabolites in multiple biological processes spanning animals, plants, bacteria, and fungi. We report on analytical methods for measuring TMA and TMAO and attempt to critically synthesize and map the global functions of TMA and TMAO in a systems biology framework.

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

Metabolomics of Gastric Cancer

Gastric cancer is the fourth most common malignancy worldwide and the third leading cause of cancer deaths. Recent metabolomics research has advanced our understanding of the relationship between metabolic reprogramming and gastric cancer progression and led to the discovery of metabolic targets for potential clinical applications and therapeutic interventions. As a powerful tool for metabolite and flux measurement, metabolomics not only allows a comprehensive analysis of metabolites and related metabolic pathways but also can investigate the interactions between gastric cancer cells and tumour microenvironment as well as between the cancer cells and gastric microbiome. In this chapter, we aim to summarize the recent advances in gastric cancer metabolism and discuss the applications of metabolomics for target discovery in gastric cancer.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Sulfadiazine Sodium Ameliorates the Metabolomic Perturbation in Mice Infected with Toxoplasma gondii

In this study, we analyzed the global metabolomic changes associated with Toxoplasma gondii infection in mice in the presence or absence of sulfadiazine sodium (SDZ) treatment. BALB/c mice were infected with T. gondii GT1 strain and treated orally with SDZ (250 μg/ml in water) for 12 consecutive days. Mice showed typical manifestations of illness at 20 days postinfection (dpi); by 30 dpi, 20% had survived and developed latent infection. We used ultraperformance liquid chromatography-mass spectrometry to profile the serum metabolomes in control (untreated and uninfected) mice, acutely infected mice, and SDZ-treated and infected mice. Infection induced significant perturbations in the metabolism of α-linolenic acid, purine, pyrimidine, arginine, tryptophan, valine, glycerophospholipids, and fatty acyls. However, treatment with SDZ seemed to alleviate the serum metabolic alterations caused by infection. The restoration of the serum metabolite levels in the treated mice was associated with better clinical outcomes. These data indicate that untargeted metabolomics can reveal biochemical pathways associated with restoration of the metabolic status of T. gondii-infected mice following SDZ treatment and could be used to monitor responses to SDZ treatment. This study provides a new systems approach to elucidate the metabolic and therapeutic effects of SDZ in the context of murine toxoplasmosis.

Assessing the suitability of capillary electrophoresis-mass spectrometry for biomarker discovery in plasma-based metabolomics

The actual utility of capillary electrophoresis-mass spectrometry (CE-MS) for biomarker discovery using metabolomics still needs to be assessed. Therefore, a simulated comparative metabolic profiling study for biomarker discovery by CE-MS was performed, using pooled human plasma samples with spiked biomarkers. Two studies have been carried out in this work. Focus of study I was on comparing two sets of plasma samples, in which one set (class I) was spiked with five isotope-labeled compounds, whereas another set (class II) was spiked with six different isotope-labeled compounds. In study II, focus was also on comparing two sets of plasma samples, however, the isotope-labeled compounds were spiked to both class I and class II samples but with concentrations which differ by a factor two between both classes (with one compound absent in each class). The aim was to determine whether CEMS-based metabolomics could reveal the spiked biomarkers as the main classifiers, applying two different data analysis software tools (MetaboAnalyst and Matlab). Unsupervised analysis of the recorded metabolic profiles revealed a clear distinction between class I and class II plasma samples in both studies. This classification was mainly attributed to the spiked isotope-labeled compounds, thereby emphasizing the utility of CE-MS for biomarker discovery.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

Monitoring for Response to Antineoplastic Drugs: The Potential of a Metabolomic Approach

For most cancers, chemotherapeutic options are rapidly expanding, providing the oncologist with substantial choices. Therefore, there is a growing need to select the best systemic therapy, for any individual, that effectively halts tumor progression with minimal toxicity. Having the capability to predict benefit and to anticipate toxicity would be ideal, but remains elusive at this time. An alternative approach is an adaptive approach that involves close observation for treatment response and emergence of resistance. Currently, response to systemic therapy is estimated using radiographic tests. Unfortunately, radiographic estimates of response are imperfect and radiographic signs of response can be delayed. This is particularly problematic for targeted agents, as tumor shrinkage is often not apparent with these drugs. As a result, patients are exposed to prolonged courses of toxic drugs that may ultimately be found to be ineffective. A biomarker-based adaptive strategy that involves the serial analysis of the metabolome is attractive. The metabolome changes rapidly with changes in physiology. Changes in the circulating metabolome associated with various antineoplastic agents have been described, but further work will be required to understand what changes signify clinical benefit. We present an investigative approach for the discovery and validation of metabolomic response biomarkers, which consists of serial analysis of the metabolome and linkage of changes in the metabolome to measurable therapeutic benefit. Potential pitfalls in the development of metabolomic biomarkers of response and loss of response are reviewed.

N-Acetylgalactosamine-Targeted Delivery of Dendrimer-Doxorubicin Conjugates Influences Doxorubicin Cytotoxicity and Metabolic Profile in Hepatic Cancer Cells

This study describes the development of targeted, doxorubicin (DOX)-loaded generation 5 (G5) polyamidoamine dendrimers able to achieve cell-specific DOX delivery and release into the cytoplasm of hepatic cancer cells. G5 is functionalized with poly(ethylene glycol) (PEG) brushes displaying N-acetylgalactosamine (NAcGal) ligands to target hepatic cancer cells. DOX is attached to G5 through one of two aromatic azo-linkages, L3 or L4, achieving either P1 ((NAcGal_β -PEGc)_16.6 -G5-(L3-DOX)_11.6 ) or P2 ((NAcGal_β -PEGc)_16.6 -G5-(L4-DOX)_13.4 ) conjugates. After confirming the conjugates' biocompatibility, flow cytometry studies show P1/P2 achieve 100% uptake into hepatic cancer cells at 30-60 × 10^-9 m particle concentration. This internalization correlates with cytotoxicity against HepG2 cells with 50% inhibitory concentration (IC₅₀ ) values of 24.8, 1414.0, and 237.8 × 10^-9 m for free DOX, P1, and P2, respectively. Differences in cytotoxicity prompted metabolomics analysis to identify the intracellular release behavior of DOX. Results show that P1/P2 release alternative DOX metabolites than free DOX. Stable isotope tracer studies show that the different metabolites induce different effects on metabolic cycles. Namely, free DOX reduces glycolysis and increases fatty acid oxidation, while P1/P2 increase glycolysis, likely as a response to high oxidative stress. Overall, P1/P2 conjugates offer a platform drug delivery technology for improving hepatic cancer therapy.

Metabolomics approach to serum biomarker for loperamide-induced constipation in SD rats

Loperamide has long been known as an opioid-receptor agonist useful as a drug for treatment of diarrhea resulting from gastroenteritis or inflammatory bowel disease as well as to induce constipation. To determine and characterize putative biomarkers that can predict constipation induced by loperamide treatment, alteration of endogenous metabolites was measured in the serum of Sprague Dawley (SD) rats treated with loperamide for 3 days using ¹H nuclear magnetic resonance (¹H NMR) spectral data. The amounts and weights of stool and urine excretion were significantly lower in the loperamide-treated group than the No-treated group, while the thickness of the villus, crypt layer, and muscle layer was decreased in the transverse colon of the same group. The concentrations of aspartate aminotransferase (AST), lactate dehydrogenase (LDH) and creatinine (Cr) were also slightly changed in the loperamide-treated group, although most of the serum components were maintained at a constant level. Furthermore, pattern recognition of endogenous metabolites showed completely separate clustering of the serum analysis parameters between the No-treated group and loperamide-treated group. Among 35 endogenous metabolites, four amino acids (alanine, glutamate, glutamine and glycine) and six endogenous metabolites (acetate, glucose, glycerol, lactate, succinate and taurine) were dramatically decreased in loperamide-treated SD rats. These results provide the first data pertaining to metabolic changes in SD rats with loperamide-induced constipation. Additionally, these findings correlate the changes in 10 metabolites with constipation.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Metabolomic approach to evaluating adriamycin pharmacodynamics and resistance in breast cancer cells

Continuous exposure of breast cancer cells to adriamycin induces high expression of P-gp and multiple drug resistance. However, the biochemical process and the underlying mechanisms for the gradually induced resistance are not clear. To explore the underlying mechanism and evaluate the anti-tumor effect and resistance of adriamycin, the drug-sensitive MCF-7S and the drug-resistant MCF-7Adr breast cancer cells were used and treated with adriamycin, and the intracellular metabolites were profiled using gas chromatography mass spectrometry. Principal components analysis of the data revealed that the two cell lines showed distinctly different metabolic responses to adriamycin. Adriamycin exposure significantly altered metabolic pattern of MCF-7S cells, which gradually became similar to the pattern of MCF-7Adr, indicating that metabolic shifts were involved in adriamycin resistance. Many intracellular metabolites involved in various metabolic pathways were significantly modulated by adriamycin treatment in the drug-sensitive MCF-7S cells, but were much less affected in the drug-resistant MCF-7Adr cells. Adriamycin treatment markedly depressed the biosynthesis of proteins, purines, pyrimidines and glutathione, and glycolysis, while it enhanced glycerol metabolism of MCF-7S cells. The elevated glycerol metabolism and down-regulated glutathione biosynthesis suggested an increased reactive oxygen species (ROS) generation and a weakened ability to balance ROS, respectively. Further studies revealed that adriamycin increased ROS and up-regulated P-gp in MCF-7S cells, which could be reversed by N-acetylcysteine treatment. It is suggested that adriamycin resistance is involved in slowed metabolism and aggravated oxidative stress. Assessment of cellular metabolomics and metabolic markers may be used to evaluate anti-tumor effects and to screen for candidate anti-tumor agents.