Identification of Insulin Resistance Biomarkers in Metabolic Syndrome Detected by UHPLC-ESI-QTOF-MS

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Plasma metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients

Recently, numerous studies have reported on different predictive models of disease severity in COVID-19 patients. Herein, we propose a highly predictive model of disease severity by integrating routine laboratory findings and plasma metabolites including cytosine as a potential biomarker of COVID-19 disease severity. One model was developed and internally validated on the basis of ROC-AUC values. The predictive accuracy of the model was 0.996 (95% CI: 0.989 to 1.000) with an optimal cut-off risk score of 3 from among 6 biomarkers including five lab findings (D-dimer, ferritin, neutrophil counts, Hp, and sTfR) and one metabolite (cytosine). The model is of high predictive power, needs a small number of variables that can be acquired at minimal cost and effort, and can be applied independent of non-empirical clinical data. The metabolomics profiling data and the modeling work stemming from it, as presented here, could further explain the cause of COVID-19 disease prognosis and patient management.

Mass spectrometry-based metabolomics approach and in vitro assays revealed promising role of 2,3-dihydroquinazolin-4(1H)-one derivatives against colorectal cancer cell lines

Colorectal cancer (CRC) is the most frequent form of gastrointestinal cancer and one of the major causes of human mortality worldwide. Many of the current CRC therapies have limitations due to multidrug resistance and/or severe side effects. Quinazoline derivatives are promising lead compounds with a wide range of pharmacological actions. In this study, the effect of seven synthesized 2,3-dihydroquinazolin-4(1H)-one analogues as potential anticancer agents against two CRC cell lines (HCT116 and SW480) was investigated using cell viability proliferation, migration, adhesion and invasion assays. A liquid chromatography-mass spectrometry (LC-MS/MS) metabolomics approach was used to identify the underlying biochemical pathways disturbed in treated-HCT116 cells. Cell viability proliferation assay revealed that four compounds (C2, C3, C5, and C7) had IC₅₀ < 10 µM with C5 displaying the most potent cytotoxic effect (IC₅₀ 1.4 and 0.3 µM against HCT116 and SW480, respectively). Additionally, the compounds showed suppression of wound closure after 72 h, and both C2 and C5 significantly decreased the number of adherent cells and suppressed HCT116 cells invasion. Metabolomics study revealed that C5 induced significant perturbations in the level of several metabolites including spermine, polyamines, glutamine, creatine and carnitine, and altered biochemical processes essential for cell proliferation and progression such as amino acids biosynthesis and metabolism, redox homeostasis, energy related processes (e.g., fatty acid oxidation, second Warburg like effect) and one-carbon metabolism. Our findings indicate that 2,3-dihydroquinazolin-4(1H)-one analogues, particularly C5, have promising anticancer properties, and shed light on the role of metabolomics in identifying new therapeutic targets and providing better understanding of the pathways altered in treated cancer cells.

MOTS-c Functionally Prevents Metabolic Disorders

Mitochondrial-derived peptides are a family of peptides encoded by short open reading frames in the mitochondrial genome, which have regulatory effects on mitochondrial functions, gene expression, and metabolic homeostasis of the body. As a new member of the mitochondrial-derived peptide family, mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) is regarding a peptide hormone that could reduce insulin resistance, prevent obesity, improve muscle function, promote bone metabolism, enhance immune regulation, and postpone aging. MOTS-c plays these physiological functions mainly through activating the AICAR-AMPK signaling pathways by disrupting the folate-methionine cycle in cells. Recent studies have shown that the above hormonal effect can be achieved through MOTS-c regulating the expression of genes such as GLUT4, STAT3, and IL-10. However, there is a lack of articles summarizing the genes and pathways involved in the physiological activity of MOTS-c. This article aims to summarize and interpret the interesting and updated findings of MOTS-c-associated genes and pathways involved in pathological metabolic processes. Finally, it is expected to develop novel diagnostic markers and treatment approaches with MOTS-c to prevent and treat metabolic disorders in the future.

Evaluation of Two Simultaneous Metabolomic and Proteomic Extraction Protocols Assessed by Ultra-High-Performance Liquid Chromatography Tandem Mass Spectrometry

Untargeted multi-omics analysis of plasma is an emerging tool for the identification of novel biomarkers for evaluating disease prognosis, and for developing a better understanding of molecular mechanisms underlying human disease. The successful application of metabolomic and proteomic approaches relies on reproducibly quantifying a wide range of metabolites and proteins. Herein, we report the results of untargeted metabolomic and proteomic analyses from blood plasma samples following analyte extraction by two frequently-used solvent systems: chloroform/methanol and methanol-only. Whole blood samples were collected from participants (n = 6) at University Hospital Sharjah (UHS) hospital, then plasma was separated and extracted by two methods: (i) methanol precipitation and (ii) 4:3 methanol:chloroform extraction. The coverage and reproducibility of the two methods were assessed by ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). The study revealed that metabolite extraction by methanol-only showed greater reproducibility for both metabolomic and proteomic quantifications than did methanol/chloroform, while yielding similar peptide coverage. However, coverage of extracted metabolites was higher with the methanol/chloroform precipitation.

Decreased Hyocholic Acid and Lysophosphatidylcholine Induce Elevated Blood Glucose in a Transgenic Porcine Model of Metabolic Disease

(1) Background: This work aims to investigate the metabolomic changes in PIGinH11 pigs and investigate differential compounds as potential therapeutic targets for metabolic diseases. (2) Methods: PIGinH11 pigs were established with a CRISPR/Cas9 system. PNPLA3^I148M, hIAPP, and GIPR^dn were knocked in the H11 locus of the pig genome. The differential metabolites between and within groups were compared at baseline and two months after high-fat-high-sucrose diet induction. (3) Results: 72.02% of the 815 detected metabolites were affected by the transgenic effect. Significantly increased metabolites included isoleucine, tyrosine, methionine, oxoglutaric acid, acylcarnitine, glucose, sphinganines, ceramides, and phosphatidylserines, while fatty acids and conjugates, phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins were decreased. Lower expression of GPAT3 and higher expression of PNPLA3^I148M decreased the synthesis of diacylglycerol and phosphatidylcholines. Accumulated ceramides that block Akt signaling and decrease hyocholic acid and lysophosphatidylcholines might be the main reason for increased blood glucose in PIGinH11 pigs, which was consistent with metabolomic changes in patients. (4) Conclusions: Through serum metabolomics and lipidomics studies, significant changes in obesity and diabetes-related biomarkers were detected in PIGinH11 pigs. Excessive fatty acids β-oxidation interfered with glucose and amino acids catabolism and reduced phosphatidylcholines. Decreased hyocholic acid, lysophosphatidylcholine, and increased ceramides exacerbated insulin resistance and elevated blood glucose. Phosphatidylserines were also increased, which might promote chronic inflammation by activating macrophages.

Mass Spectrometry-Based Proteomic and Metabolomic Profiling of Serum Samples for Discovery and Validation of Tuberculosis Diagnostic Biomarker Signature

Tuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which the discovery of effective diagnostic biomarkers is crucial. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into three experimental groups—healthy controls (controls), latent TB infection (LTBI), and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate, and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between controls and TB patients. The AUC, specificity, and sensitivity, determined by ROC statistical analysis of the model composed of four of these proteins considering both proteomic sets, were 0.96, 93%, and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine, and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes were submitted to ROC analysis. An AUC = 1 was determined, with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling one protein and four metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature correctly assigned the 12 controls and 12 patients used only for prediction (AUC = 1, specificity = 100%, and sensitivity = 100%). This multiomics approach revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.

Discovery and Validation of Potential Serum Biomarkers with Pro-Inflammatory and DNA Damage Activities in Ulcerative Colitis: A Comprehensive Untargeted Metabolomic Study

Ulcerative colitis is a type of non-specific inflammatory bowel disease with unclear etiology. It is considered a progressive disease with risks of bowel motility disorders, anorectal dysfunction, and even colorectal cancer. Commonly used diagnostic markers have poor specificity and cannot predict the development of ulcerative colitis. In this study, 77 serum samples (31 patients, 46 healthy controls) were analyzed using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry and 31 metabolites with significant level changes were found, revealing the relationship of ulcerative colitis to disturbed glutathione metabolism and caffeine metabolism. In addition, pyroglutamic acid, a biomarker of cervical cancer and gastric cancer, was identified with elevated levels in the serum of ulcerative colitis patients. The role of pyroglutamic acid was further analyzed, and the results indicated its positive correlation with the upregulation of inflammatory factors and increased levels of phosphorylated histone H2AX (γH2AX) in IEC-6 cells, which are related to DNA damage. All these results suggest that pyroglutamic acid is not only a biomarker for distinguishing ulcerative colitis status, but that it is also a potential effective metabolite that promotes the transformation of ulcerative colitis to colorectal cancer.