Prostate cancer (PCa) is the second most common and a leading cause of cancer-related deaths among men. Current screening methods lack precision in distinguishing aggressive cases, emphasizing a need for tissue-based biomarkers. Although Golgi disorganization, ER stress, and elevated high-mannose (Man) glycoproteins (e.g., Integrin αv, key metastatic player) are recognized features of metastatic prostate tumors, their interrelationships remain unexplored. It is observed that the growth of primary prostate tumors is linked to an increase in endoplasmic reticulum (ER)-plasma membrane (PM) junctions signaling, mediated by STIM1 and ORP5. However, transition to lymph node and tissue metastasis is associated with their downregulation, loss of ER-PM communications, significant Golgi dispersal, and rapid conversion of high-Man glycans in the Golgi to atypical MGAT5-modified sugars that facilitate Integrin αv clustering at the PM via Galectin-3 binding. Golgi dispersal is associated with increased organelle volume and surface area to accommodate heightened trafficking and processing. These findings position STIM1 and ORP5 as biomarkers of aggressive PCa and show that high-Man enrichment is not due to defective maturation but reflects a glycan pool that cancer cells actively utilize, suggesting that the concept of ER stress response in PCa should be redefined to include Golgi reorganization and altered ER-PM junctions.

Nuclear magnetic resonance (NMR) spectroscopy is a fundamental tool of metabolomics, valued for its reproducibility, quantitative accuracy and broad applicability across biological, chemical and clinical sciences. However, methodological inconsistencies, insufficient protocol reporting and limited infrastructure continue to hinder reproducibility and data sharing. To assess the current state of NMR metabolomics practice, we developed a comprehensive questionnaire and distributed it worldwide to researchers engaged in NMR-based metabolomics. We received 75 responses from a diverse cohort of investigators from academia, clinics and core facilities. The survey focused on Quality Assurance (QA) and Quality Control (QC) practices and provides an overview of the current status of NMR metabolomics and its implementation. Results reveal that while 86% of laboratories have Standard Operating Procedures (SOPs), deviations from these protocols are common and often undocumented, undermining reproducibility. QC practices, including pooled samples and system suitability checks, are widely recognized, but their implementation is inconsistent. Data accessibility remains limited, with fewer than 10% of respondents routinely depositing raw or processed spectral data in public repositories. Formal regulatory oversight and dedicated QA personnel are uncommon. Training is largely informal, with substantial gaps in areas such as data analysis and statistics, raising concerns about knowledge transfer and methodological consistency. Our findings describe a technically skilled community that is constrained by variations in NMR infrastructure and inconsistent implementation of best practices. Addressing these issues through adaptive standardization, structured training programs, and stronger institutional support is critical for advancing transparency, reproducibility and impact of NMR in metabolomics.

Abstract Metabolic crosstalk between cell types is critical to our mechanistic understanding of pancreatic cancer. The lack of methods to investigate metabolic crosstalk has impeded the comprehensive OMIC characterization of these complex cellular systems. To address this need, an NMR metabolomics method employing a selective isotope-labeling strategy is presented to study metabolic crosstalk in routine cell cultures. 2D 1H–13C HSQC NMR experiments were used to simultaneously monitor pancreatic cancer cells and stromal cells, which revealed a cancer-cell-induced metabolic remodeling of stromal cells. Cancer-cell-conditioned medium was used to stimulate stromal cells, which identified a unique and divergent metabolism in stromal cells in contrast to the classical Warburg metabolism in cancer cells. The two phenotypically different stromal cells exhibited distinct stimulated metabolic profiles; however, the exo-metabolome from both stimulated stromal cells revealed that 13C6-glucose-derived carbon is excreted as alanine and pyruvate. Furthermore, intracellular 13C6-glucose was converted to a variety of tricarboxylic acid cycle-linked and redox metabolites. Stromal cells, unlike cancer cells, mainly convert 13C3-pyruvate to alanine and lactate at an early growth time point, followed by the incorporation of 13C3-pyruvate-derived carbon into multiple amino acids. Interestingly, unstimulated stromal cells depend on pyruvate for survival, whereas cancer cells do not. Simply put, efficient metabolic crosstalk between cancer and stromal cells enables the cells to utilize different metabolic pathways and nutrient sources. Although presented to study cancer-stromal crosstalk, the method can be easily adapted to a variety of cellular crosstalk systems, such as neuron-astrocyte, host–pathogen, or host-microbiota systems.

The pituitary gonadotropin luteinizing hormone (LH) is the primary stimulus for ovulation, luteal formation and progesterone synthesis, regardless of species. Despite increased awareness of intracellular signaling events initiating the massive production of progesterone during the reproductive cycle and pregnancy, critical gaps exist in our knowledge of the metabolic and lipidomic pathways required for initiating and maintaining luteal progesterone synthesis. Using untargeted metabolomics and metabolic flux analysis in primary steroidogenic luteal cells, evidence is provided for rapid LHCGR-stimulation of metabolic pathways leading to increased glycolysis and oxygen consumption. Treatment with LH stimulated post-translational modifications of enzymes involved in de novo lipogenesis. Mechanistic studies implicated a crucial role for de novo fatty acid synthesis and fatty acid oxidation in energy homeostasis, LHCGR/PKA signaling, and, consequently, progesterone production. These findings reveal novel hormone-sensitive metabolic pathways essential for maintaining LHCGR/PKA signaling and steroidogenesis. Understanding hormonal control of metabolic pathways in steroidogenic cells may help elucidate approaches for improving ovarian function and successful reproduction or identifying metabolic targets for developing nonhormonal contraceptives.

Metabolomics has been successfully applied to study neurological and neurodegenerative disorders, including Parkinson's disease for the following purposes: (1) identifying potential biomarkers of onset and disease progression; (2) identifying novel mechanisms of disease progression; and (3) assessing treatment prognosis and outcome. Reproducible and efficient extraction of metabolites is imperative to the success of any metabolomics investigation. Unlike other OMICS techniques, the composition of the metabolome can be negatively impacted by the preparation, processing, and handling of these samples. The proper choice of data collection, preprocessing, and processing protocols are similarly important to the design of an effective metabolomics experiment. Likewise, correctly applying univariate and multivariate statistical methods is essential for providing biologically relevant insights. In this chapter, we have outlined a detailed metabolomics workflow that addresses these issues. A step-by-step protocol from preparing neuronal cells and metabolomic tissue samples to their metabolic analyses using nuclear magnetic resonance, mass spectrometry, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and chemometrics is presented.

Metabolomics is a rapidly growing multidisciplinary field with ever increasing demand and usability, which is attracting a surge of new researchers. While their varied skill sets, scientific questions, and approaches enrich the field with fresh perspectives and innovation, individual investigators also bring wide-ranging levels of metabolomics-specific experience and diverse areas of interest. These factors introduce considerable variability and inconsistency in both the methodology and reporting. A recent comparative literature review of nuclear magnetic resonance (NMR) metabolomics from studies published in 2010 and 2020 revealed significant shortcomings in the reporting of experimental details necessary for evaluating both the scientific rigor and the reproducibility of NMR-based metabolomics experiments. Each stage of metabolomics research contains multiple methodological choices and various optimization parameters, all of which can introduce experimental bias and alter the study results. This emphasizes the need for proper reporting to enhance reproducibility, data reusability, and study comparability. To address these concerns, the NMR Special Interest Group within the Metabolomics Association of North America presents reporting recommendations focused on fundamental aspects of NMR metabolomics research identified from the detailed literature review report. These include specifics with respect to study design, sample preparation, data acquisition, data processing and analysis, data accessibility, and comparability to previous studies. Also presented is a complementary list of seminal papers in the field to guide the study design and implementation of NMR metabolomics experiments. This initiative seeks to enhance the long-term impact of NMR metabolomics by supporting high-quality, reproducible, and impactful data collected from well-executed and thoroughly reported studies.

Acute radiation syndrome (ARS) occurs when hematopoietic or gastrointestinal cells are damaged by radiation exposure causing DNA damage to the bone marrow and gastrointestinal epithelial stem cell populations. In these highly proliferative cell types, DNA damage inhibits stem cell repopulation. In humans and animals, this inability to regenerate stem cells is lethal. Within this manuscript, several compounds, Amifostine, Captopril, Ciprofloxacin, PrC-210, 5-AED (5-androstene-3β,17β-diol), and 5-AET (5-androstene-3β,7β,17B-triol), are assessed for their ability to protect against ARS in an in vitro and/or in vivo setting. ARS was accomplished by irradiating mouse bone marrow cells or rat intestinal epithelial (IEC-6) cells in vitro with 4–8 Gy and in vivo by exposing Mus musculus to 7.3 Gy of whole-body irradiation. The primary endpoints of this study include cellular viability, DNA damage via γ-H2AX, colony formation, and overall survival at 30-days post-irradiation. In addition to evaluating the radioprotective performance of each compound, this study establishes a distinct set of in vitro assays to predict the overall efficacy of potential radioprotectors in an in vivo model of ARS. Furthermore, these results highlight the need for FDA-approved medical intervention to protect against ARS.

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

ScopeThere is a lack of research comparing how different protein isolates influence the microbiome, especially when carbohydrate (CHO) availability is varied. The objective is to determine changes in gut microbiota composition and function during fermentation of digested protein isolates under high and low CHO conditions.Methods and resultsProtein isolates from beef, egg white, milk, pea, and soy are subjected to in vitro digestion and fermentation with human fecal microbiota. Under low CHO conditions, the microbiota is primarily proteolytic with decreased concentrations of peptides and increased variance among microbial taxa and production of ammonia and branched chain fatty acids by the microbiota. Milk protein not only results in the highest production of butyrate and p‐hydroxyphenylacetate but also has high concentrations of deleterious fermentation metabolites. Amino acid composition of the protein isolates is significantly correlated with abundances of many microbial taxa and metabolites, but the correlations are stronger in the low CHO medium.ConclusionThis study shows that low CHO conditions increase proteolytic fermentation and result in increased differences in microbiota response to protein isolates. It also showed that amino acid composition is highly associated with microbiota composition and function especially under low CHO conditions.

A literature survey was conducted to identify current practices used by NMR metabolomics investigators when conducting and reporting their metabolomics studies. A total of 463 papers from 2020 to 80 papers from 2010 were selected from PubMed and were manually analyzed by a team of investigators to assess the extent and completeness of the experimental procedures and protocols reported. A significant number of the papers did not report on essential experimental details, incompletely stated which statistical methods were used, improperly applied supervised multivariate statistical analyses, or lacked validation of statistical models. A large diversity of protocols and software were identified, which suggests a lack of consensus and a relatively limited use of commonly agreed upon standards for conducting and reporting NMR metabolomics studies. The overall intent of the survey is to inform and encourage the NMR metabolomics community to develop and adopt best practices for the field.

The ability of tumour cells to thrive in harsh microenvironments depends on the utilization of nutrients available in the milieu. Here we show that pancreatic cancer-associated fibroblasts (CAFs) regulate tumour cell metabolism through the secretion of acetate, which can be blocked by silencing ATP citrate lyase (ACLY) in CAFs. We further show that acetyl-CoA synthetase short-chain family member 2 (ACSS2) channels the exogenous acetate to regulate the dynamic cancer epigenome and transcriptome, thereby facilitating cancer cell survival in an acidic microenvironment. Comparative H3K27ac ChIP–seq and RNA–seq analyses revealed alterations in polyamine homeostasis through regulation of SAT1 gene expression and enrichment of the SP1-responsive signature. We identified acetate/ACSS2-mediated acetylation of SP1 at the lysine 19 residue that increased SP1 protein stability and transcriptional activity. Genetic or pharmacologic inhibition of the ACSS2–SP1–SAT1 axis diminished the tumour burden in mouse models. These results reveal that the metabolic flexibility imparted by the stroma-derived acetate enabled cancer cell survival under acidosis via the ACSS2–SP1–SAT1 axis. Murthy et al. demonstrate that cancer-associated fibroblast-derived acetate regulates polyamine homeostasis via an ACSS2–SP1–SAT1 axis in pancreatic cancer cells, thus enabling cell survival and tumour development under acidosis.

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes. Evaluating how sample preparation methods and data analysis platforms impact measured metabolite concentrations underscores the need for a universal consensus in the followed approaches for metabolomics data analysis.

In (2008), McMorris and Powers proved that a hierarchical version of majority rule is the only consensus function satisfying restricted decisive neutrality, symmetry, monotonicity, and is biprofile nontrivial. We show that this result can be extended to any finite median semilattice X such that X is not a lattice, the height of X is greater than one, X contains at least two atoms, and X satisfies a condition we call special.

Progesterone production by the corpus luteum is fundamental for establishing and maintaining pregnancy. The pituitary gonadotropin luteinizing hormone (LH) is recognized as the primary stimulus for luteal formation and progesterone synthesis, regardless of species. Previous studies demonstrated an elevation in abundance of genes related to glucose and lipid metabolism during the follicular to luteal transition. However, the metabolic phenotype of these highly steroidogenic cells has not been studied. Herein, we determined acute metabolic changes induced by LH in primary luteal cells and defined pathways required for progesterone synthesis. Untargeted metabolomics analysis revealed that LH induces rapid changes in vital metabolic pathways, including glycolysis, tricarboxylic acid (TCA) cycle, pentose phosphate pathway, de novo lipogenesis, and hydrolysis of phospholipids. LH stimulated glucose uptake, enhanced glycolysis, and flux of [U- 13 C 6 ]-labeled glucose-derived carbons into metabolic branches associated with adenosine 5'-triphosphate (ATP) and NADH/NADPH production, synthesis of nucleotides, proteins, and lipids, glycosylation of proteins or lipids, and redox homeostasis. Selective use of small molecule inhibitors targeting the most significantly changed pathways, such as glycolysis, TCA cycle, and lipogenesis, uncovered cellular metabolic routes required for LH-stimulated steroidogenesis. Furthermore, LH via the protein kinase A (PKA) pathway triggered post- translational modification of acetyl-CoA carboxylase alpha (ACACA) and ATP citrate lyase (ACLY), enzymes involved in de novo synthesis of fatty acids. Inhibition of ACLY and fatty acid transport into mitochondria reduced LH-stimulated ATP, cAMP production, PKA activation, and progesterone synthesis. Taken together, these findings reveal novel hormone-sensitive metabolic pathways essential for maintaining LHCGR/PKA signaling and steroidogenesis in ovarian luteal cells. Significance:The establishment and maintenance of pregnancy require a well-developed corpus luteum, an endocrine gland within the ovary that produces progesterone. Although there is increased awareness of intracellular signaling events initiating the massive production of progesterone during the reproductive cycle and pregnancy, there are critical gaps in our knowledge of the metabolic and lipidomic pathways required for initiating and maintaining luteal progesterone synthesis. Here, we describe rapid, hormonally triggered metabolic pathways, and define metabolic targets crucial for progesterone synthesis by ovarian steroidogenic cells. Understanding hormonal control of metabolic pathways may help elucidate approaches for improving ovarian function and successful reproduction or identifying metabolic targets for developing nonhormonal contraceptives.

Metabolomics commonly relies on using one-dimensional (1D) H-1 NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation <= 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Cancer is a leading cause of world-wide death and a major subject of clinical studies focused on the identification of new diagnostic tools. An in-depth meta-analysis of 244 clinical metabolomics studies of human serum samples highlights a reproducibility crisis. A total of 2,206 unique metabolites were reported as statistically significant across the 244 studies, but 72% (1,582) of these metabolites were identified by only one study. Further analysis shows a random disparate disagreement in reported directions of metabolite concentration changes when detected by multiple studies. Statistical models revealed that 1,867 of the 2,206 metabolites (85%) are simply statistical noise. Only 3 to 12% of these metabolites reach the threshold of statistical significance for a specific cancer type. Our findings demonstrate the absence of a detectable metabolic response to cancer and provide evidence of a serious need by the metabolomics community to establish widely accepted best practices to improve future outcomes.

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105–106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.