Molecular Choreography of Acute Exercise.

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

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 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.

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/. Supplementary information: Supplementary data are available at Bioinformatics online.

Age-dependent changes in metabolite profile and lipid saturation in dystrophic mice.

Duchenne Muscular Dystrophy (DMD) is a fatal X-linked genetic disorder. In DMD, the absence of the dystrophin protein causes decreased sarcolemmal integrity resulting in progressive replacement of muscle with fibrofatty tissue. The effects of lacking dystrophin on muscle and systemic metabolism are still unclear. Therefore, to determine the impact of the absence of dystrophin on metabolism, we investigated the metabolic and lipid profile at two different, well-defined stages of muscle damage and stabilization in mdx mice. We measured NMR-detectable metabolite and lipid profiles in the serum and muscles of mdx mice at 6 and 24 weeks of age. Metabolites were determined in muscle in vivo using 1 H MRI/MRS, in isolated muscles using 1 H-HR-MAS NMR, and in serum using high resolution 1 H/13 C NMR. Dystrophic mice were found to have a unique lipid saturation profile compared with control mice, revealing an age-related metabolic change. In the 6-week-old mdx mice, serum lipids were increased and the degree of lipid saturation changed between 6 and 24 weeks. The serum taurine-creatine ratio increased over the life span of mdx, but not in control mice. Furthermore, the saturation index of lipids increased in the serum but decreased in the tissue over time. Finally, we demonstrated associations between MRI-T2 , a strong indicator of inflammation/edema, with tissue and serum lipid profiles. These results indicate the complex temporal changes of metabolites in the tissue and serum during repetitive bouts of muscle damage and regeneration that occur in dystrophic muscle.

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.

Near-Infrared Optical Imaging Noninvasively Detects Acutely Damaged Muscle.

Muscle damage is currently assessed through methods such as muscle biopsy, serum biomarkers, functional testing, and imaging procedures, each with its own inherent limitations, and a pressing need for a safe, repeatable, inexpensive, and noninvasive modality to assess the state of muscle health remains. Our aim was to develop and assess near-infrared (NIR) optical imaging as a novel noninvasive method of detecting and quantifying muscle damage. An immobilization-reambulation model was used for inducing muscle damage and recovery in the lower hindlimbs in mice. Confirmation of muscle damage was obtained using in vivo indocyanine green-enhanced NIR optical imaging, magnetic resonance imaging, and ex vivo tissue analysis. The soleus of the immobilized-reambulated hindlimb was found to have a greater amount of muscle damage compared to that in the contralateral nonimmobilized limb, confirmed by in vivo indocyanine green-enhanced NIR optical imaging (3.86-fold increase in radiant efficiency), magnetic resonance imaging (1.41-fold increase in T2), and an ex vivo spectrophotometric assay of indocyanine green uptake (1.87-fold increase in normalized absorbance). Contrast-enhanced NIR optical imaging provides a sensitive, rapid, and noninvasive screening method that can be used for imaging and quantifying muscle damage and recovery in vivo.

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.

Multi-omics microsampling for the profiling of lifestyle-associated changes in health.

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 µl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Global, distinctive, and personal changes in molecular and microbial profiles by specific fibers in humans.

Dietary fibers act through the microbiome to improve cardiovascular health and prevent metabolic disorders and cancer. To understand the health benefits of dietary fiber supplementation, we investigated two popular purified fibers, arabinoxylan (AX) and long-chain inulin (LCI), and a mixture of five fibers. We present multiomic signatures of metabolomics, lipidomics, proteomics, metagenomics, a cytokine panel, and clinical measurements on healthy and insulin-resistant participants. Each fiber is associated with fiber-dependent biochemical and microbial responses. AX consumption associates with a significant reduction in LDL and an increase in bile acids, contributing to its observed cholesterol reduction. LCI is associated with an increase in Bifidobacterium. However, at the highest LCI dose, there is increased inflammation and elevation in the liver enzyme alanine aminotransferase. This study yields insights into the effects of fiber supplementation and the mechanisms behind fiber-induced cholesterol reduction, and it shows effects of individual, purified fibers on the microbiome.

Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes.

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

Systematic Identification of Regulators of Oxidative Stress Reveals Non-canonical Roles for Peroxisomal Import and the Pentose Phosphate Pathway.

Reactive oxygen species (ROS) play critical roles in metabolism and disease, yet a comprehensive analysis of the cellular response to oxidative stress is lacking. To systematically identify regulators of oxidative stress, we conducted genome-wide Cas9/CRISPR and shRNA screens. This revealed a detailed picture of diverse pathways that control oxidative stress response, ranging from the TCA cycle and DNA repair machineries to iron transport, trafficking, and metabolism. Paradoxically, disrupting the pentose phosphate pathway (PPP) at the level of phosphogluconate dehydrogenase (PGD) protects cells against ROS. This dramatically alters metabolites in the PPP, consistent with rewiring of upper glycolysis to promote antioxidant production. In addition, disruption of peroxisomal import unexpectedly increases resistance to oxidative stress by altering the localization of catalase. Together, these studies provide insights into the roles of peroxisomal matrix import and the PPP in redox biology and represent a rich resource for understanding the cellular response to oxidative stress.

Global metabolic profiling to model biological processes of aging in twins.

Aging is intimately linked to system-wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross-sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long-chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R2  = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out-of-bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age-related variability in metabolic profiles in response to environmental exposure.

The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight.

To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress-related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.

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.

An overview of methods using (13)C for improved compound identification in metabolomics and natural products.

Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography-mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward. We have designed and implemented several NMR and LC-MS approaches that utilize (13)C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) (13)C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5 and 95% channels. For NMR applications, we describe two (13)C-based approaches. For samples at natural abundance, we have developed a workflow to obtain (13)C-(13)C and (13)C-(1)H statistical correlations using 1D (13)C and (1)H NMR spectra. For samples that can be isotopically labeled, we describe another NMR approach to obtain direct (13)C-(13)C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which (13)C NMR can be used to identify unknown compounds from IROA experiments. By combining technologies with the same samples, we can identify important biomarkers and corresponding metabolites of interest.

The renin-angiotensin system and prevention of age-related functional decline: where are we now?

Declining physical function is a major health problem for older adults as it is associated with multiple comorbidities and mortality. Exercise has been shown to improve physical function, though response to exercise is variable. Conversely, drugs targeting the renin-angiotensin system (RAS) pathway, including angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs), are also reported to improve physical function. In the past decade, significant strides have been made to understand the complexity and specificity of the RAS system as it pertains to physical function in older adults. Prior findings have also determined that interactions between antihypertensive medications and exercise may influence physical function above and beyond either factor alone. We review the latest research on RAS, exercise, and physical function for older adults. We also outline future research aims in this area, including genetic influences and clinical phenotyping, for the purpose of maintaining or improving physical function through tailored treatments.