Search for: All records

Bladder cancer (BC) is frequent cancer affecting the urinary tract and is one of the most prevalent malignancies worldwide. No biomarkers that can be used for effective monitoring of therapeutic interventions for this cancer have been identified to date. This study investigated polar metabolite profiles in urine samples from 100 BC patients and 100 normal controls (NCs) using nuclear magnetic resonance (NMR) and two methods of high- resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS). Five urine metabolites were identified and quantified using NMR spectroscopy to be potential indicators of bladder cancer. Twenty-five LDI-MS-detected compounds, predominantly peptides and lipids, distinguished urine samples from BC and NCs individuals. Level changes of three characteristic urine metabolites enabled BC tumor grades to be distinguished, and ten metabolites were reported to correlate with tumor stages. Receiver-Operating Characteristics analysis showed high predictive power for all three types of metabolomics data, with the area under the curve (AUC) values greater than 0.87. These findings suggest that metabolite markers identified in this study may be useful for the non-invasive detection and monitoring of bladder cancer stages and grades. 

Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates.

We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins.

RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

 

Arsenic is a toxic metalloid with differential biological effects, depending on speciation and concentration. Trivalent arsenic (arsenite, AsIII) is more toxic at lower concentrations than the pentavalent form (arsenate, AsV). In E. coli, the proteins encoded by the arsRBC operon are the major arsenic detoxification mechanism. Our previous transcriptional analyses indicate broad changes in metal uptake and regulation upon arsenic exposure. Currently, it is not known how arsenic exposure impacts the cellular distribution of other metals. This study examines the metalloproteome of E. coli strains with and without the arsRBC operon in response to sublethal doses of AsIII and AsV. Size exclusion chromatography coupled with inductively coupled plasma mass spectrometry (SEC-ICPMS) was used to investigate the distribution of five metals (56Fe, 24Mg, 66Zn, 75As, and 63Cu) in proteins and protein complexes under native conditions. Parallel analysis by SEC-UV-Vis spectroscopy monitored the presence of protein cofactors. Together, these data reveal global changes in the metalloproteome, proteome, protein cofactors, and soluble intracellular metal pools in response to arsenic stress in E. coli. This work brings to light one outcome of metal exposure and suggests that metal toxicity on the cellular level arises from direct and indirect effects. 

Central metabolism has a profound impact on the clinical phenotypes and penetrance of neurological diseases such as Alzheimer’s (AD) and Parkinson’s (PD) diseases, Amyotrophic Lateral Sclerosis (ALS) and Autism Spectrum Disorder (ASD). In contrast to the multifactorial origin of these neurological diseases, neurodevelopmental impairment and neurodegeneration in Familial Dysautonomia (FD) results from a single point mutation in the ELP1 gene. FD patients represent a well-defined population who can help us better understand the cellular networks underlying neurodegeneration, and how disease traits are affected by metabolic dysfunction, which in turn may contribute to dysregulation of the gut–brain axis of FD. Here, 1H NMR spectroscopy was employed to characterize the serum and fecal metabolomes of FD patients, and to assess similarities and differences in the polar metabolite profiles between FD patients and healthy relative controls. Findings from this work revealed noteworthy metabolic alterations reflected in energy (ATP) production, mitochondrial function, amino acid and nucleotide catabolism, neurosignaling molecules, and gut-microbial metabolism. These results provide further evidence for a close interconnection between metabolism, neurodegeneration, and gut microbiome dysbiosis in FD, and create an opportunity to explore whether metabolic interventions targeting the gut–brain–metabolism axis of FD could be used to redress or slow down the progressive neurodegeneration observed in FD patients.

 

Copper is an essential trace metal serving as a cofactor in innate immunity, metabolism, and iron transport. We hypothesize that copper deficiency may influence survival in patients with cirrhosis through these pathways.

We performed a retrospective cohort study involving 183 consecutive patients with cirrhosis or portal hypertension. Copper from blood and liver tissues was measured using inductively coupled plasma mass spectrometry. Polar metabolites were measured using nuclear magnetic resonance spectroscopy. Copper deficiency was defined by serum or plasma copper below 80 µg/dL for women or 70 µg/dL for men.

The prevalence of copper deficiency was 17% (N=31). Copper deficiency was associated with younger age, race, zinc and selenium deficiency, and higher infection rates (42% vs. 20%,p=0.01). Serum copper correlated positively with albumin, ceruloplasmin, hepatic copper, and negatively with IL-1β. Levels of polar metabolites involved in amino acids catabolism, mitochondrial transport of fatty acids, and gut microbial metabolism differed significantly according to copper deficiency status. During a median follow-up of 396 days, mortality was 22.6% in patients with copper deficiency compared with 10.5% in patients without. Liver transplantation rates were similar (32% vs. 30%). Cause-specific competing risk analysis showed that copper deficiency was associated with a significantly higher risk of death before transplantation after adjusting for age, sex, MELD-Na, and Karnofsky score (HR: 3.40, 95% CI, 1.18–9.82,p=0.023).

In advanced cirrhosis, copper deficiency is relatively common and is associated with an increased infection risk, a distinctive metabolic profile, and an increased risk of death before transplantation.

 

Hypoglycemia may be induced by a variety of physiologic and pathologic stimuli and can result in life-threatening consequences if untreated. However, hypoglycemia may also play a role in the purported health benefits of intermittent fasting and caloric restriction. Previously, we demonstrated that systemic administration of ricin toxin induced fatal hypoglycemia in mice. Here, we examine the metabolic landscape of the hypoglycemic state induced in the liver of mice by two different stimuli: systemic ricin administration and fasting. Each stimulus produced the same decrease in blood glucose and weight loss. The polar metabolome was studied using 1H NMR, quantifying 59 specific metabolites, and untargeted LC-MS on approximately 5000 features. Results were analyzed by multivariate analyses, using both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), to identify global metabolic patterns, and by univariate analyses (ANOVA) to assess individual metabolites. The results demonstrated that while there were some similarities in the responses to the two stimuli including decreased glucose, ADP, and glutathione, they elicited distinct metabolic states. The metabolite showing the greatest difference was O-phosphocholine, elevated in ricin-treated animals and known to be affected by the pro-inflammatory cytokine TNF-α. Another difference was the alternative fuel source utilized, with fasting-induced hypoglycemia primarily ketotic, while the response to ricin-induced hypoglycemia involves protein and amino acid catabolism.

 

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spec- trometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, iso- butyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metab- olites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC. 

The rise in bacterial resistance to common antibiotics has raised an increased need for alternative treatment strategies. The natural antibacterial product, 18β-glycyrrhetinic acid (GRA) has shown efficacy against community-associated methicillin-resistant Staphylococcus aureus (MRSA), although its interactions against planktonic and biofilm modes of growth remain poorly understood. This investigation utilized biochemical and metabolic approaches to further elucidate the effects of GRA on MRSA. Prolonged exposure of planktonic MRSA cell cultures to GRA resulted in increased production of staphyloxanthin, a pigment known to exhibit antioxidant and membrane-stabilizing functions. Then, 1D 1H NMR analyses of intracellular metabolite extracts from MRSA treated with GRA revealed significant changes in intracellular polar metabolite profiles, including increased levels of succinate and citrate, and significant reductions in several amino acids, including branch chain amino acids. These changes reflect the MRSA response to GRA exposure, including potentially altering its membrane composition, which consumes branched chain amino acids and leads to significant energy expenditure. Although GRA itself had no significant effect of biofilm viability, it seems to be an effective biofilm disruptor. This may be related to interference with cell–cell aggregation, as treatment of planktonic MRSA cultures with GRA leads to a significant reduction in micro-aggregation. The dispersive nature of GRA on MRSA biofilms may prove valuable for treatment of such infections and could be used to increase susceptibility to complementary antibiotic therapeutics. 

Familial dysautonomia (FD) is a rare genetic neurologic disorder caused by impaired neuronal development and progressive degeneration of both the peripheral and central nervous systems. FD is monogenic, with >99.4% of patients sharing an identical point mutation in the elongator acetyltransferase complex subunit 1 (ELP1) gene, providing a relatively simple genetic background in which to identify modifiable factors that influence pathology. Gastrointestinal symptoms and metabolic deficits are common among FD patients, which supports the hypothesis that the gut microbiome and metabolome are altered and dysfunctional compared to healthy individuals. Here we show significant differences in gut microbiome composition (16 S rRNA gene sequencing of stool samples) and NMR-based stool and serum metabolomes between a cohort of FD patients (~14% of patients worldwide) and their cohabitating, healthy relatives. We show that key observations in human subjects are recapitulated in a neuron-specificElp1-deficient mouse model, and that cohousing mutant and littermate control mice ameliorates gut microbiome dysbiosis, improves deficits in gut transit, and reduces disease severity. Our results provide evidence that neurologic deficits in FD alter the structure and function of the gut microbiome, which shifts overall host metabolism to perpetuate further neurodegeneration.

 

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021—the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.