Purpose The aim of this study is to generate a metabolic database for biomedical studies of biopsy specimens by high‐resolution magic angle spinning (HRMAS) nuclear MR (NMR). Methods Seventy‐six metabolites, classically found in human biopsy samples, were prepared in aqueous solution at a known concentration and analyzed by HRMAS NMR. The spectra were recorded under the same conditions as the ones used for the analysis of biopsy specimens routinely performed in our hospital. Results For each metabolite, a complete set of NMR spectra (1D 1 H, 1D 1 H‐CPMG, 2D J‐Resolved, 2D TOCSY, and 2D 1 H‐ 13 C HSQC) was recorded at 500 MHz and 277 K. All spectra were manually assigned using the information contained in the different spectra and existing databases. Experiments to measure the T 1 and the T 2 of the different protons present in the 76 metabolites were also recorded. Conclusion This new HRMAS metabolic database is a useful tool for all scientists working on human biopsy specimens, particularly in the field of oncology. It will make the identification of metabolites in biopsy specimens faster and more reliable. Additionally, the knowledge of the T 1 and T 2 values will allow to obtain a more accurate quantification of the metabolites present in biopsy specimens.

Matrix metalloproteinase 11 (MMP11) is an extracellular proteolytic enzyme belonging to the matrix metalloproteinase (MMP11) family. These proteases are involved in extracellular matrix (ECM) remodeling and activation of latent factors. MMP11 is a negative regulator of adipose tissue development and controls energy metabolism in vivo. In cancer, MMP11 expression is associated with poorer survival, and preclinical studies in mice showed that MMP11 accelerates tumor growth. How the metabolic role of MMP11 contributes to cancer development is poorly understood. To address this issue, we developed a series of preclinical mouse mammary gland tumor models by genetic engineering. Tumor growth was studied in mice either deficient (Loss of Function-LOF) or overexpressing MMP11 (Gain of Function-GOF) crossed with a transgenic model of breast cancer induced by the polyoma middle T antigen (PyMT) driven by the murine mammary tumor virus promoter (MMTV) (MMTV-PyMT). Both GOF and LOF models support roles for MMP11, favoring early tumor growth by increasing proliferation and reducing apoptosis. Of interest, MMP11 promotes Insulin-like Growth Factor-1 (IGF1)/protein kinase B (AKT)/Forkhead box protein O1 (FoxO1) signaling and is associated with a metabolic switch in the tumor, activation of the endoplasmic reticulum stress response, and an alteration in the mitochondrial unfolded protein response with decreased proteasome activity. In addition, high resonance magic angle spinning (HRMAS) metabolomics analysis of tumors from both models established a metabolic signature that favors tumorigenesis when MMP11 is overexpressed. These data support the idea that MMP11 contributes to an adaptive metabolic response, named metabolic flexibility, promoting cancer growth.

Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra (1 H-13 C). Unfortunately, this analysis requires much longer time and prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the 1 H and 13 C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the 13 C dimension by just performing 1 H HRMAS NMR experiment. We show on a rat model of central nervous system tissues (80 samples, 5 tissues) that our methods achieve 0.971 and 0.957 mean R 2 values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 groups of 39 metabolites with 97% accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1 H dimension. In practice, this information can provide valuable feedback to the surgeon to further resect the cavity to avoid potential recurrence.

The use of 13 C-labeled molecular probes is essential to explore altered metabolic pathways in human pathologies. The analysis of the different 13 C isotopologues resulting from these changes in metabolic pathways is essential to understand the different biological processes involved. We propose an NMR methodology consisting of eight different NMR experiments performed under HRMAS conditions to explore metabolic pathways in unprocessed pathological cells and tissues. This methodology has the potential to study human pathologies in the medical field and to enable the analysis of the mode of action of therapeutic treatments.