Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale modeling.

Anaerobic microbial metabolism drives critical functions within global ecosystems, host-microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable 13C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen's genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine's biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-13C]glucose and [15N]leucine, confirming the formation of [13C,15N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Transmission of Alzheimer's disease-associated microbiota dysbiosis and its impact on cognitive function: evidence from mice and patients.

Spouses of Alzheimer's disease (AD) patients are at a higher risk of developing incidental dementia. However, the causes and underlying mechanism of this clinical observation remain largely unknown. One possible explanation is linked to microbiota dysbiosis, a condition that has been associated with AD. However, it remains unclear whether gut microbiota dysbiosis can be transmitted from AD individuals to non-AD individuals and contribute to the development of AD pathogenesis and cognitive impairment. We, therefore, set out to perform both animal studies and clinical investigation by co-housing wild-type mice and AD transgenic mice, analyzing microbiota via 16S rRNA gene sequencing, measuring short-chain fatty acid amounts, and employing behavioral test, mass spectrometry, site-mutations and other methods. The present study revealed that co-housing between wild-type mice and AD transgenic mice or administrating feces of AD transgenic mice to wild-type mice resulted in AD-associated gut microbiota dysbiosis, Tau phosphorylation, and cognitive impairment in the wild-type mice. Gavage with Lactobacillus and Bifidobacterium restored these changes in the wild-type mice. The oral and gut microbiota of AD patient partners resembled that of AD patients but differed from healthy controls, indicating the transmission of microbiota. The underlying mechanism of these findings includes that the butyric acid-mediated acetylation of GSK3ß at lysine 15 regulated its phosphorylation at serine 9, consequently impacting Tau phosphorylation. Pending confirmative studies, these results provide insight into a potential link between the transmission of AD-associated microbiota dysbiosis and development of cognitive impairment, which underscore the need for further research in this area.

Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions.

High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.

Visualizing metabolomics data with R.

In communicating scientific results, convincing data visualization is of utmost importance. Especially in metabolomics, results based on large numbers of dimensions and variables necessitate particular attention in order to convey their message unambiguously to the reader; also, in the era of open science, traceability and reproducibility are becoming increasingly important. This article describes the use of the R programming language to visualize published metabolomics data resulting from ex vivo NMR spectroscopy and mass spectrometry experiments with a special focus on reproducibility, including example figures as well as associated R code for ease of reuse. Examples include various types of plots (bar plots, swarm plots, and violin plots; volcano plots, heatmaps, Euler diagrams, Kaplan-Meier survival plots) and annotations (groupings, intragroup line connections, significance brackets, text annotations). Advantages of code-generated plots as well as advanced techniques and best practices are discussed.

Designing a quality assurance process for quality control of nuclear magnetic resonance metabolomics studies of human blood.

High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR)-based metabolomics has demonstrated its utility in studies of biofluids for various diseases. HRMAS NMR spectroscopy is uniquely well suited for analyzing human blood samples because of the small quantity of samples and minimal preparation required. To develop this methodology into standardized clinical protocols, establishment of the method's quality assurance (QA) and evaluations of its quality control (QC) are critical. This study aims to assess the QA/QC measured from human blood specimens in the form of serum and plasma through within-subject and between-subject comparisons, as well as stability and consistency comparisons over several freezing-thawing cycles of sample storage conditions, and most importantly, the agreement of pooled control samples against individual samples.

13 C NMR quantification of polyamine syntheses in rat prostate.

Currently, many prostate cancer patients, detected through the prostate specific antigen test, harbor organ-confined indolent disease that cannot be differentiated from aggressive cancer according to clinically and pathologically known measures. Spermine has been considered as an endogenous inhibitor for prostate-confined cancer growth and its expression has shown correlation with prostate cancer growth rates. If established clinically, measurements of spermine bio-synthesis rates in prostates may predict prostate cancer growth and patient outcomes. Using rat models, we tested the feasibility of quantifying spermine bio-synthesis rates with 13 C NMR. Male Copenhagen rats (10 weeks, n = 6) were injected with uniformly 13 C-labeled L-ornithine HCl, and were sacrificed in pairs at 10, 30, and 60 min after injection. Another two rats were injected with saline and sacrificed at 30 min as controls. Prostates were harvested and extracted with perchloric acid and the neutralized solutions were examined by 13 C NMR at 600 MHz. 13 C NMR revealed measurable ornithine, as well as putrescine-spermidine-spermine syntheses in rat prostates, allowing polyamine bio-synthetic and ornithine bio-catabolic rates to be calculated. Our study demonstrated the feasibility of 13 C NMR for measuring bio-synthesis rates of ornithine to spermine enzymatic reactions in rat prostates. The current study established a foundation upon which future investigations of protocols that differentiate prostate cancer growth rates according to the measure of ornithine to spermine bio-synthetic rates may be developed.

Total ice content and lipid saturation determine adipose tissue cryolipolysis by injection of ice-slurry.

OBJECTIVES: Cryolipolysis uses tissue cooling to solidify lipids, preferentially damaging lipid-rich cells. Topical cooling is popular for the reduction of local subcutaneous fat. Injection of biocompatible ice-slurry is a recently introduced alternative. We developed and verified a quantitative model that simulates the heat exchange and phase changes involved, offering insights into ice-slurry injection for treating subcutaneous fat. METHODS: Finite element method was used to model the spatial and temporal progression of heat transfer between adipose tissue and injected ice-slurry, estimating dose-response relationships between properties of the slurry and size of tissue affected by cryolipolysis. Phase changes of both slurry and adipose tissue lipids were considered. An in vivo swine model was used to validate the numerical solutions. Oils with different lipid compositions were exposed to ice-slurry in vitro to evaluate the effects of lipid freezing temperature. Microscopy and nuclear magnetic resonance (NMR) were performed to detect lipid phase changes. RESULTS: A ball of granular ice was deposited at the injection site in subcutaneous fat. Total injected ice content determines both the effective cooling region of tissue, and the duration of tissue cooling. Water's high latent heat of fusion enables tissue cooling long after slurry injection. Slurry temperature affects the rate of tissue cooling. In swine, when 30 ml slurry injection at -3.5°C was compared to 15 ml slurry injection at -4.8°C (both with the same total ice content), the latter led to almost twice faster tissue cooling. NMR showed a large decrease in diffusion upon lipid crystallization; saturated lipids with higher freezing temperatures were more susceptible to solidification after ice-slurry injection. CONCLUSIONS: Total injected ice content determines both the volume of tissue treated by cryolipolysis and the cooling duration after slurry injection, while slurry temperature affects the cooling rate. Lipid saturation, which varies with diet and anatomic location, also has an important influence.

Differentiation of patients with and without prostate cancer using urine 1 H NMR metabolomics.

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. For its detection, serum prostate-specific antigen (PSA) screening is commonly used, despite its lack of specificity, high false positive rate, and inability to discriminate indolent from aggressive PCa. Following increases in serum PSA levels, clinicians often conduct prostate biopsies with or without advanced imaging. Nuclear magnetic resonance (NMR)-based metabolomics has proven to be promising for advancing early-detection and elucidation of disease progression, through the discovery and characterization of novel biomarkers. This retrospective study of urine-NMR samples, from prostate biopsy patients with and without PCa, identified several metabolites involved in energy metabolism, amino acid metabolism, and the hippuric acid pathway. Of note, lactate and hippurate-key metabolites involved in cellular proliferation and microbiome effects, respectively-were significantly altered, unveiling widespread metabolomic modifications associated with PCa development. These findings support urine metabolomics profiling as a promising strategy to identify new clinical biomarkers for PCa detection and diagnosis.

High resolution magic angle spinning MRS in prostate cancer.

INTRODUCTION: Prostate cancer (PCa) is one of the leading causes of death among men worldwide. The current methods utilized to screen for prostate cancer may not have sufficient sensitivity in distinguishing aggressive from indolent diseases, which affect the quality of life of patients in the short and long term. The overdiagnosis of cases and overtreatment are prevalent due to the heterogeneity of the disease in terms of latent and progressive variants, as well as in the tissue types present in biopsy samples. METHODS: The purpose of this review is to discuss the potential clinical benefits of incorporating high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS) modalities to overcome the current challenges in the diagnosis, prognostication, and monitoring of PCa.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer.

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

Applications of high-resolution magic angle spinning MRS in biomedical studies I-cell line and animal models.

High-resolution magic angle spinning (HRMAS) MRS allows for direct measurements of non-liquid tissue and cell specimens to present valuable insights into the cellular metabolisms of physiological and pathological processes. HRMAS produces high-resolution spectra comparable to those obtained from solutions of specimen extracts but without complex metabolite extraction processes, and preserves the tissue cellular structure in a form suitable for pathological examinations following spectroscopic analysis. The technique has been applied in a wide variety of biomedical and biochemical studies and become one of the major platforms of metabolomic studies. By quantifying single metabolites, metabolite ratios, or metabolic profiles in their entirety, HRMAS presents promising possibilities for diagnosis and prediction of clinical outcomes for various diseases, as well as deciphering of metabolic changes resulting from drug therapies or xenobiotic interactions. In this review, we evaluate HRMAS MRS results on animal models and cell lines reported in the literature, and present the diverse applications of the method for the understanding of pathological processes and the effectiveness of therapies, development of disease animal models, and new progress in HRMAS methodology.

Applications of high-resolution magic angle spinning MRS in biomedical studies II-Human diseases.

High-resolution magic angle spinning (HRMAS) MRS is a powerful method for gaining insight into the physiological and pathological processes of cellular metabolism. Given its ability to obtain high-resolution spectra of non-liquid biological samples, while preserving tissue architecture for subsequent histopathological analysis, the technique has become invaluable for biochemical and biomedical studies. Using HRMAS MRS, alterations in measured metabolites, metabolic ratios, and metabolomic profiles present the possibility to improve identification and prognostication of various diseases and decipher the metabolomic impact of drug therapies. In this review, we evaluate HRMAS MRS results on human tissue specimens from malignancies and non-localized diseases reported in the literature since the inception of the technique in 1996. We present the diverse applications of the technique in understanding pathological processes of different anatomical origins, correlations with in vivo imaging, effectiveness of therapies, and progress in the HRMAS methodology.

Tissue Characterization with Quantitative High-Resolution Magic Angle Spinning Chemical Exchange Saturation Transfer Z-Spectroscopy.

Chemical exchange saturation transfer (CEST) provides sensitive magnetic resonance (MR) contrast for probing dilute compounds via exchangeable protons, serving as an emerging molecular imaging methodology. CEST Z-spectrum is often acquired by sweeping radiofrequency saturation around bulk water resonance, offset by offset, to detect CEST effects at characteristic chemical shift offsets, which requires prolonged acquisition time. Herein, combining high-resolution magic angle spinning (HRMAS) with concurrent application of gradient and rf saturation to achieve fast Z-spectral acquisition, we demonstrated the feasibility of fast quantitative HRMAS CEST Z-spectroscopy. The concept was validated with phantoms, which showed excellent agreement with results obtained from conventional HRMAS MR spectroscopy (MRS). We further utilized the HRMAS Z-spectroscopy for fast ex vivo quantification of ischemic injury with rodent brain tissues after ischemic stroke. This method allows rapid and quantitative CEST characterization of biological tissues and shows potential for a host of biomedical applications.

High-resolution magic angle spinning 1H MRS in prostate cancer.

Prostate cancer (PCa) is the most frequently diagnosed malignancy in men worldwide, largely as a result of the increased use of the annual serum prostate-specific antigen (PSA) screening test for detection. PSA screening has saved lives, but it has also resulted in the overtreatment of many patients with PCa because of a limited ability to accurately localize and characterize PCa lesions through imaging. High-resolution magic angle spinning (HRMAS) (1)H MRS has proven to be a strong potential clinical tool for PCa diagnosis and prognosis. The HRMAS technique allows valuable metabolic information to be obtained from ex vivo intact tissue samples and also enables the performance of histopathology on the same tissue specimens. Studies have found that the quantification of individual metabolite levels and metabolite ratios, as well as metabolomic profiles, shows strong potential to improve accuracy in PCa detection, diagnosis and monitoring. Ex vivo HRMAS is also a valuable tool for the interpretation of in vivo results, including the localization of tumors, and thus has the potential to improve in vivo diagnostic tests used in the clinic. Here, we primarily review publications of HRMAS (1)H MRS and its use for the study of intact human prostate tissue.