¹³C NMR metabolomics: applications at natural abundance.

(13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality (13)C NMR spectra obtained using a custom (13)C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D (13)C and (1)H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful (13)C-(13)C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of (13)C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The (13)C and (1)H data together led to 15 matches in the database compared to just 7 using (1)H alone, and the (13)C correlated peak lists had far fewer false positives than the (1)H generated lists. In addition, the (13)C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/

13C/31P MRS Metabolic Biomarkers of Disease Progression and Response to AAV Delivery of hGAA in a Mouse Model of Pompe Disease

The development of therapeutic clinical trials for glycogen storage disorders, including Pompe disease, has called for non-invasive and objective biomarkers. Glycogen accumulation can be measured in vivo with 13C MRS. However, clinical implementation remains challenging due to low signal-to-noise. On the other hand, the buildup of glycolytic intermediates may be detected with 31P MRS. We sought to identify new biomarkers of disease progression in muscle using 13C/31P MRS and 1H HR-MAS in a mouse model of Pompe disease (Gaa−/−). We evaluated the sensitivity of these MR biomarkers in vivo after treatment using an adeno-associated virus vector 2/9 encoding hGAA driven by the desmin promotor. 31P MRS showed significantly elevated phosphomonoesters (PMEs) in Gaa−/− compared to control at 2 (0.06 ± 0.02 versus 0.03 ± 0.01; p = 0.003), 6, 12, and 18 months of age. Correlative 1H HR-MAS measures in intact gastrocnemius muscles revealed high glucose-6-phosphate (G-6-P). After intramuscular AAV injections, glycogen, PME, and G-6-P were decreased within normal range. The changes in PME levels likely partly resulted from changes in G-6-P, one of the overlapping phosphomonoesters in the 31P MR spectra in vivo. Because 31P MRS is inherently more sensitive than 13C MRS, PME levels have greater potential as a clinical biomarker and should be considered as a complementary approach for future studies in Pompe patients

¹³C NMR Metabolomics: Applications at Natural Abundance

¹³C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality ¹³C NMR spectra obtained using a custom ¹³C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D ¹³C and ¹H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, <i>Drosophila melanogaster</i> extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful ¹³C–¹³C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of ¹³C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The ¹³C and ¹H data together led to 15 matches in the database compared to just 7 using ¹H alone, and the ¹³C correlated peak lists had far fewer false positives than the ¹H generated lists. In addition, the ¹³C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the <i>D. melanogaster</i> extracts and mouse serum

Contrast-Enhanced Near-Infrared Optical Imaging Detects Exacerbation and Amelioration of Murine Muscular Dystrophy

Assessment of muscle pathology is a key outcome measure to measure the success of clinical trials studying muscular dystrophies; however, few robust minimally invasive measures exist. Indocyanine green (ICG)-enhanced near-infrared (NIR) optical imaging offers an objective, minimally invasive, and longitudinal modality that can quantify pathology within muscle by imaging uptake of ICG into the damaged muscles. Dystrophic mice lacking dystrophin (mdx) or gamma-sarcoglycan (Sgcg −/− ) were compared to control mice by NIR optical imaging and magnetic resonance imaging (MRI). We determined that optical imaging could be used to differentiate control and dystrophic mice, visualize eccentric muscle induced by downhill treadmill running, and restore the membrane integrity in Sgcg −/− mice following adeno-associated virus (AAV) delivery of recombinant human SGCG (desAAV8hSGCG). We conclude that NIR optical imaging is comparable to MRI and can be used to detect muscle damage in dystrophic muscle as compared to unaffected controls, monitor worsening of muscle pathology in muscular dystrophy, and assess regression of pathology following therapeutic intervention in muscular dystrophies

Longitudinal multi-omics of host-microbe dynamics in prediabetes.

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D&nbsp;better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states