The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes

Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this field. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics.

Urinary Metabolic Markers of Bladder Cancer: A Reflection of the Tumor or the Response of the Body?

This work will review the metabolic information that various studies have obtained in recent years on bladder cancer, with particular attention to discovering biomarkers in urine for the diagnosis and prognosis of this disease. In principle, they would be capable of complementing cystoscopy, an invasive but nowadays irreplaceable technique or, in the best case, of replacing it. We will evaluate the degree of reproducibility that the different experiments have shown in the indication of biomarkers, and a synthesis will be attempted to obtain a consensus list that is more likely to become a guideline for clinical practice. In further analysis, we will inquire into the origin of these dysregulated metabolites in patients with bladder cancer. For this purpose, it will be helpful to compare the imbalances measured in urine with those known inside tumor cells or tissues. Although the urine analysis is sometimes considered a liquid biopsy because of its direct contact with the tumor in the bladder wall, it contains metabolites from all organs and tissues of the body, and the tumor is separated from urine by the most impermeable barrier found in mammals. The distinction between the specific and systemic responses can help understand the disease and its consequences in more depth.

Clustering subtypes of breast cancer by combining immunohistochemistry profiles and metabolism characteristics measured using FDG PET/CT

Background The aim of this study was to investigate the effect of combining immunohistochemical profiles and metabolic information to characterize breast cancer subtypes. Methods This retrospective study included 289 breast tumors from 284 patients who underwent preoperative 18 F-fluorodeoxyglucose (FDG) positron emission tomography/ computed tomography (PET/CT). Molecular subtypes of breast cancer were classified as Hormonal, HER2, Dual (a combination of both Hormonal and HER2 features), and triple-negative (TN). Histopathologic findings and immunohistochemical results for Ki-67, EGFR, CK 5/6, and p53 were also analyzed. The maximum standardized uptake value (SUV) measured from FDG PET/CT was used to evaluate tumoral glucose metabolism. Results Overall, 182, 24, 47, and 36 tumors were classified as Hormonal, HER2, Dual, and TN subtypes, respectively. Molecular profiles of tumor aggressiveness and the tumor SUV revealed a gradual increase from the Hormonal to the TN type. The tumor SUV was significantly correlated with tumor size, expression levels of p53, Ki-67, and EGFR, and nuclear grade (all p < 0.001). In contrast, the tumor SUV was negatively correlated with the expression of estrogen receptors (r = − 0.234, p < 0.001) and progesterone receptors (r = − 0.220, p < 0.001). Multiple linear regression analysis revealed that histopathologic markers explained tumor glucose metabolism (adjusted R-squared value 0.238, p < 0.001). Tumor metabolism can thus help define breast cancer subtypes with aggressive/adverse prognostic features. Conclusions Metabolic activity measured using FDG PET/CT was significantly correlated with the molecular alteration profiles of breast cancer assessed using immunohistochemical analysis. Combining molecular markers and metabolic information may aid in the recognition and understanding of tumor aggressiveness in breast cancer and be helpful as a prognostic marker.

Current human brain applications and challenges of dynamic hyperpolarized carbon-13 labeled pyruvate MR metabolic imaging

The ability of hyperpolarized carbon-13 MR metabolic imaging to acquire dynamic metabolic information in real time is crucial to gain mechanistic insights into metabolic pathways, which are complementary to anatomic and other functional imaging methods. This review presents the advantages of this emerging functional imaging technology, describes considerations in clinical translations, and summarizes current human brain applications. Despite rapid development in methodologies, significant technological and physiological related challenges continue to impede broader clinical translation.

Utility of 3T single-voxel proton MR spectroscopy for differentiating intracranial meningiomas from intracranial enhanced mass lesions

Background Proton magnetic resonance spectroscopy (MRS) provides structural and metabolic information that is useful for the diagnosis of meningiomas with atypical radiological appearance. However, the metabolite that should be prioritized for the diagnosis of meningiomas has not been established. Purpose To evaluate the differences between the metabolic peaks of meningiomas and other intracranial enhanced mass lesions (non-meningiomas) using MR spectroscopy in short echo time (TE) spectra and the most useful metabolic peak for discriminating between the groups. Material and Methods The study involved 9 meningiomas, 22 non-meningiomas, intracranial enhancing tumors and abscesses, and 15 normal controls. The ranking of the peak at 3.8 ppm, peak at 3.8 ppm/Creatine (Cr), β-γ Glutamine-Glutamate (bgGlx)/Cr, N-acetyl compounds (NACs)/Cr, choline (Cho)/Cr, lipid and/or lactate (Lip-Lac) at 1.3 ppm/Cr, and the presence of alanine (Ala) were derived. The metabolic peaks were compared using the Mann-Whitney U test. ROC analysis was used to determine the cut-off values for differentiating meningiomas from non-meningiomas using statistically significant metabolic peaks. Results The ranking of the peak at 3.8 ppm among all the peaks, peak at 3.8 ppm/Cr, bgGlx/Cr, Lip-Lac/Cr, and the presence of Ala discriminated meningiomas from non-meningiomas with moderate to high accuracy. The highest accuracy was 96.9% at a threshold value of 3 for the rank of the peak at 3.8 ppm. Conclusion A distinct elevated peak at 3.8 ppm, ranked among the top three highest peaks, allowed the detection of meningiomas.

Plant Metabolic Network: A multi-species resource of plant metabolic information

Plant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.One-sentence SummaryThe Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.

Hyperpolarized 13C-glucose magnetic resonance highlights reduced aerobic glycolysis in vivo in infiltrative glioblastoma

Glioblastoma (GBM) is the most aggressive brain tumor type in adults. GBM is heterogeneous, with a compact core lesion surrounded by an invasive tumor front. This front is highly relevant for tumor recurrence but is generally non-detectable using standard imaging techniques. Recent studies demonstrated distinct metabolic profiles of the invasive phenotype in GBM. Magnetic resonance (MR) of hyperpolarized 13C-labeled probes is a rapidly advancing field that provides real-time metabolic information. Here, we applied hyperpolarized 13C-glucose MR to mouse GBM models. Compared to controls, the amount of lactate produced from hyperpolarized glucose was higher in the compact GBM model, consistent with the accepted “Warburg effect”. However, the opposite response was observed in models reflecting the invasive zone, with less lactate produced than in controls, implying a reduction in aerobic glycolysis. These striking differences could be used to map the metabolic heterogeneity in GBM and to visualize the infiltrative front of GBM.

Design of a Coplanar Interlayer Gapped Microstrips Arrangement for Multi-Nuclei (1H, 19F, 31P, and 23Na) Applications in 7T MRI

Seven Tesla Magnetic Resonance (MR) systems can obtain high quality anatomical images using protons (1H) and can be used for multinuclear imaging and MR spectroscopy. These imaging modes can also obtain images and metabolic information using other nuclei, such as 19F, 31P, and 23Na. Here, we present an RF coil unit using a microstrip capable of resonating at four frequencies: 300 (1H), 280 (19F), 121 (31P), and 78 (23Na) MHz. The RF unit consists of a single feeding port and four lines that resonate and run a current at their respective frequency. We used the gapped microstrip concept to isolate each conducting line and interleaved the dielectric materials used for each line, thereby reducing the coupling between them. We also analyzed this design using electromagnetic (EM) simulations, and found that the quad tuned arrangement produced low coupling between adjacent current lines and achieved a uniform |B1| field in the z-y plane.