Correction: Genetic and metabolomic architecture of variation in diet restriction-mediated lifespan extension in Drosophila.

[This corrects the article DOI: 10.1371/journal.pgen.1008835.].

Raising NAD+ Level Stimulates Short-Chain Dehydrogenase/Reductase Proteins to Alleviate Heart Failure Independent of Mitochondrial Protein Deacetylation.

BACKGROUND: Strategies to increase cellular NAD+ (oxidized nicotinamide adenine dinucleotide) level have prevented cardiac dysfunction in multiple models of heart failure, but molecular mechanisms remain unclear. Little is known about the benefits of NAD+-based therapies in failing hearts after the symptoms of heart failure have appeared. Most pretreatment regimens suggested mechanisms involving activation of sirtuin, especially Sirt3 (sirtuin 3), and mitochondrial protein acetylation. METHODS: We induced cardiac dysfunction by pressure overload in SIRT3-deficient (knockout) mice and compared their response with nicotinamide riboside chloride treatment with wild-type mice. To model a therapeutic approach, we initiated the treatment in mice with established cardiac dysfunction. RESULTS: We found nicotinamide riboside chloride improved mitochondrial function and blunted heart failure progression. Similar benefits were observed in wild-type and knockout mice. Boosting NAD+ level improved the function of NAD(H) redox-sensitive SDR (short-chain dehydrogenase/reductase) family proteins. Upregulation of Mrpp2 (mitochondrial ribonuclease P protein 2), a multifunctional SDR protein and a subunit of mitochondrial ribonuclease P, improves mitochondrial DNA transcripts processing and electron transport chain function. Activation of SDRs in the retinol metabolism pathway stimulates RXRα (retinoid X receptor α)/PPARα (proliferator-activated receptor α) signaling and restores mitochondrial oxidative metabolism. Downregulation of Mrpp2 and impaired mitochondrial ribonuclease P were found in human failing hearts, suggesting a shared mechanism of defective mitochondrial biogenesis in mouse and human heart failure. CONCLUSIONS: These findings identify SDR proteins as important regulators of mitochondrial function and molecular targets of NAD+-based therapy. Furthermore, the benefit is observed regardless of Sirt3-mediated mitochondrial protein deacetylation, a widely held mechanism for NAD+-based therapy for heart failure. The data also show that NAD+-based therapy can be useful in pre-existing heart failure.

Glucocerebrosidase reduces the spread of protein aggregation in a Drosophila melanogaster model of neurodegeneration by regulating proteins trafficked by extracellular vesicles.

Abnormal protein aggregation within neurons is a key pathologic feature of Parkinson's disease (PD). The spread of brain protein aggregates is associated with clinical disease progression, but how this occurs remains unclear. Mutations in glucosidase, beta acid 1 (GBA), which encodes glucocerebrosidase (GCase), are the most penetrant common genetic risk factor for PD and dementia with Lewy bodies and associate with faster disease progression. To explore how GBA mutations influence pathogenesis, we previously created a Drosophila model of GBA deficiency (Gba1b) that manifests neurodegeneration and accelerated protein aggregation. Proteomic analysis of Gba1b mutants revealed dysregulation of proteins involved in extracellular vesicle (EV) biology, and we found altered protein composition of EVs from Gba1b mutants. Accordingly, we hypothesized that GBA may influence pathogenic protein aggregate spread via EVs. We found that accumulation of ubiquitinated proteins and Ref(2)P, Drosophila homologue of mammalian p62, were reduced in muscle and brain tissue of Gba1b flies by ectopic expression of wildtype GCase in muscle. Neuronal GCase expression also rescued protein aggregation both cell-autonomously in brain and non-cell-autonomously in muscle. Muscle-specific GBA expression reduced the elevated levels of EV-intrinsic proteins and Ref(2)P found in EVs from Gba1b flies. Perturbing EV biogenesis through neutral sphingomyelinase (nSMase), an enzyme important for EV release and ceramide metabolism, enhanced protein aggregation when knocked down in muscle, but did not modify Gba1b mutant protein aggregation when knocked down in neurons. Lipidomic analysis of nSMase knockdown on ceramide and glucosylceramide levels suggested that Gba1b mutant protein aggregation may depend on relative depletion of specific ceramide species often enriched in EVs. Finally, we identified ectopically expressed GCase within isolated EVs. Together, our findings suggest that GCase deficiency promotes accelerated protein aggregate spread between cells and tissues via dysregulated EVs, and EV-mediated trafficking of GCase may partially account for the reduction in aggregate spread.

Age-related changes of skeletal muscle metabolic response to contraction are also sex-dependent.

Mitochondria adapt to increased energy demands during muscle contraction by acutely altering metabolite fluxes and substrate oxidation. With age, an impaired mitochondrial metabolic response may contribute to reduced exercise tolerance and decreased skeletal muscle mass, specific force, increased overall fatty depositions in the skeletal muscle, frailty and depressed energy maintenance. We hypothesized that elevated energy stress in mitochondria with age alters the capacity of mitochondria to utilize different substrates following muscle contraction. To test this hypothesis, we used in vivo electrical stimulation to simulate high-intensity intervals (HII) or low intensity steady-state (LISS) exercise in young (5-7 months) and aged (27-29 months) male and female mice to characterize effects of age and sex on mitochondrial substrate utilization in skeletal muscle following contraction. Mitochondrial respiration using glutamate decreased in aged males following HII and glutamate oxidation was inhibited following HII in both the contracted and non-stimulated muscle of aged female muscle. Analyses of the muscle metabolome of female mice indicated that changes in metabolic pathways induced by HII and LISS contractions in young muscle are absent in aged muscle. To test improved mitochondrial function on substrate utilization following HII, we treated aged females with elamipretide (ELAM), a mitochondrially-targeted peptide shown to improve mitochondrial bioenergetics and restore redox status in aged muscle. ELAM removed inhibition of glutamate oxidation and showed increased metabolic pathway changes following HII, suggesting rescuing redox status and improving bioenergetic function in mitochondria from aged muscle increases glutamate utilization and enhances the metabolic response to muscle contraction in aged muscle. KEY POINTS: Acute local contraction of gastrocnemius can systemically alter mitochondrial respiration in non-stimulated muscle. Age-related changes in mitochondrial respiration using glutamate or palmitoyl carnitine following contraction are sex-dependent. Respiration using glutamate after high-intensity contraction is inhibited in aged female muscle. Metabolite level and pathway changes following muscle contraction decrease with age in female mice. Treatment with the mitochondrially-targeted peptide elamipretide can partially rescue metabolite response to muscle contraction.

Genetic and metabolomic architecture of variation in diet restriction-mediated lifespan extension in Drosophila.

In most organisms, dietary restriction (DR) increases lifespan. However, several studies have found that genotypes within the same species vary widely in how they respond to DR. To explore the mechanisms underlying this variation, we exposed 178 inbred Drosophila melanogaster lines to a DR or ad libitum (AL) diet, and measured a panel of 105 metabolites under both diets. Twenty four out of 105 metabolites were associated with the magnitude of the lifespan response. These included proteinogenic amino acids and metabolites involved in α-ketoglutarate (α-KG)/glutamine metabolism. We confirm the role of α-KG/glutamine synthesis pathways in the DR response through genetic manipulations. We used covariance network analysis to investigate diet-dependent interactions between metabolites, identifying the essential amino acids threonine and arginine as "hub" metabolites in the DR response. Finally, we employ a novel metabolic and genetic bipartite network analysis to reveal multiple genes that influence DR lifespan response, some of which have not previously been implicated in DR regulation. One of these is CCHa2R, a gene that encodes a neuropeptide receptor that influences satiety response and insulin signaling. Across the lines, variation in an intronic single nucleotide variant of CCHa2R correlated with variation in levels of five metabolites, all of which in turn were correlated with DR lifespan response. Inhibition of adult CCHa2R expression extended DR lifespan of flies, confirming the role of CCHa2R in lifespan response. These results provide support for the power of combined genomic and metabolomic analysis to identify key pathways underlying variation in this complex quantitative trait.

Evaluation of potential metabolomic-based biomarkers of protein, carbohydrate and fat intakes using a controlled feeding study.

PURPOSE: Objective biomarkers of dietary exposure are needed to establish reliable diet-disease associations. Unfortunately, robust biomarkers of macronutrient intakes are scarce. We aimed to assess the utility of serum, 24-h urine and spot urine high-dimensional metabolites for the development of biomarkers of daily intake of total energy, protein, carbohydrate and fat, and the percent of energy from these macronutrients (%E). METHODS: A 2-week controlled feeding study mimicking the participants' habitual diets was conducted among 153 postmenopausal women from the Women's Health Initiative (WHI). Fasting serum metabolomic profiles were analyzed using a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for aqueous metabolites and a direct-injection-based quantitative lipidomics platform. Urinary metabolites were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy at 800 MHz and by untargeted gas chromatography-mass spectrometry (GC-MS). Variable selection was performed to build prediction models for each dietary variable. RESULTS: The highest cross-validated multiple correlation coefficients (CV-R2) for protein intake (%E) and carbohydrate intake (%E) using metabolites only were 36.3 and 37.1%, respectively. With the addition of established dietary biomarkers (doubly labeled water for energy and urinary nitrogen for protein), the CV-R2 reached 55.5% for energy (kcal/d), 52.0 and 45.0% for protein (g/d, %E), 55.9 and 37.0% for carbohydrate (g/d, %E). CONCLUSION: Selected panels of serum and urine metabolites, without the inclusion of doubly labeled water and urinary nitrogen biomarkers, give a reliable and robust prediction of daily intake of energy from protein and carbohydrate.

Transcriptomic, proteomic, and metabolomic landscape of positional memory in the caudal fin of zebrafish.

Regeneration requires cells to regulate proliferation and patterning according to their spatial position. Positional memory is a property that enables regenerating cells to recall spatial information from the uninjured tissue. Positional memory is hypothesized to rely on gradients of molecules, few of which have been identified. Here, we quantified the global abundance of transcripts, proteins, and metabolites along the proximodistal axis of caudal fins of uninjured and regenerating adult zebrafish. Using this approach, we uncovered complex overlapping expression patterns for hundreds of molecules involved in diverse cellular functions, including development, bioelectric signaling, and amino acid and lipid metabolism. Moreover, 32 genes differentially expressed at the RNA level had concomitant differential expression of the encoded proteins. Thus, the identification of proximodistal differences in levels of RNAs, proteins, and metabolites will facilitate future functional studies of positional memory during appendage regeneration.

Multiplatform Metabolomics Investigation of Antiadipogenic Effects on 3T3-L1 Adipocytes by a Potent Diarylheptanoid.

Obesity is fast becoming a serious health problem worldwide. Of the many possible antiobesity strategies, one interesting approach focuses on blocking adipocyte differentiation and lipid accumulation to counteract the rise in fat storage. However, there is currently no drug available for the treatment of obesity that works by inhibiting adipocyte differentiation. Here we use a broad-based metabolomics approach to interrogate and better understand metabolic changes that occur during adipocyte differentiation. In particular, we focus on changes induced by the antiadipogenic diarylheptanoid, which was isolated from a traditional Chinese medicine Dioscorea zingiberensis and identified as (3 R,5 R)-3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane (1). Targeted aqueous metabolic profiling indicated that a total of 14 metabolites involved in the TCA cycle, glycolysis, amino acid metabolism, and purine catabolism participate in regulating energy metabolism, lipogenesis, and lipolysis in adipocyte differentiation and can be modulated by diarylheptanoid 1. As indicated by lipidomics analysis, diarylheptanoid 1 restored the quantity and degree of unsaturation of long-chain free fatty acids and restored the levels of 171 lipids mainly from 10 lipid classes in adipocytes. In addition, carbohydrate metabolism in diarylheptanoid-1-treated adipocytes further demonstrated the delayed differentiation process by flux analysis. Our results provide valuable information for further understanding the metabolic adjustment in adipocytes subjected to diarylheptanoid 1 treatment. Moreover, this study offers new insight into developing antiadipogenic leading compounds based on metabolomics.

Human retinal pigment epithelial cells prefer proline as a nutrient and transport metabolic intermediates to the retinal side.

Metabolite transport is a major function of the retinal pigment epithelium (RPE) to support the neural retina. RPE dysfunction plays a significant role in retinal degenerative diseases. We have used mass spectrometry with 13C tracers to systematically study nutrient consumption and metabolite transport in cultured human fetal RPE. LC/MS-MS detected 120 metabolites in the medium from either the apical or basal side. Surprisingly, more proline is consumed than any other nutrient, including glucose, taurine, lipids, vitamins, or other amino acids. Besides being oxidized through the Krebs cycle, proline is used to make citrate via reductive carboxylation. Citrate, made either from 13C proline or from 13C glucose, is preferentially exported to the apical side and is taken up by the retina. In conclusion, RPE cells consume multiple nutrients, including glucose and taurine, but prefer proline, and they actively synthesize and export metabolic intermediates to the apical side to nourish the outer retina.

NMR-Guided Mass Spectrometry for Absolute Quantitation of Human Blood Metabolites.

Broad-based, targeted metabolite profiling using mass spectrometry (MS) has become a major platform used in the field of metabolomics for a variety of applications. However, quantitative MS analysis is challenging owing to numerous factors including (1) the need for, ideally, isotope-labeled internal standards for each metabolite, (2) the fact that such standards may be unavailable or prohibitively costly, (3) the need to maintain the standards' concentrations close to those of the target metabolites, and (4) the alternative use of time-consuming calibration curves for each target metabolite. Here, we introduce a new method in which metabolites from a single serum specimen are quantified on the basis of a recently developed NMR method [ Nagana Gowda et al. Anal. Chem. 2015 , 87 , 706 ] and then used as references for absolute metabolite quantitation using MS. The MS concentrations of 30 metabolites thus derived for test serum samples exhibited excellent correlations with the NMR ones (R2 > 0.99) with a median CV of 3.2%. This NMR-guided-MS quantitation approach is simple and easy to implement and offers new avenues for the routine quantification of blood metabolites using MS. The demonstration that NMR and MS data can be compared and correlated when using identical sample preparations allows improved opportunities to exploit their combined strengths for biomarker discovery and unknown-metabolite identification. Intriguingly, however, metabolites including glutamine, pyroglutamic acid, glucose, and sarcosine correlated poorly with NMR data because of stability issues in their MS analyses or weak or overlapping signals. Such information is potentially important for improving biomarker discovery and biological interpretations. Further, the new quantitation method demonstrated here for human blood serum can in principle be extended to a variety of biological mixtures.

Fnip1 regulates skeletal muscle fiber type specification, fatigue resistance, and susceptibility to muscular dystrophy.

Mammalian skeletal muscle is broadly characterized by the presence of two distinct categories of muscle fibers called type I "red" slow twitch and type II "white" fast twitch, which display marked differences in contraction strength, metabolic strategies, and susceptibility to fatigue. The relative representation of each fiber type can have major influences on susceptibility to obesity, diabetes, and muscular dystrophies. However, the molecular factors controlling fiber type specification remain incompletely defined. In this study, we describe the control of fiber type specification and susceptibility to metabolic disease by folliculin interacting protein-1 (Fnip1). Using Fnip1 null mice, we found that loss of Fnip1 increased the representation of type I fibers characterized by increased myoglobin, slow twitch markers [myosin heavy chain 7 (MyH7), succinate dehydrogenase, troponin I 1, troponin C1, troponin T1], capillary density, and mitochondria number. Cultured Fnip1-null muscle fibers had higher oxidative capacity, and isolated Fnip1-null skeletal muscles were more resistant to postcontraction fatigue relative to WT skeletal muscles. Biochemical analyses revealed increased activation of the metabolic sensor AMP kinase (AMPK), and increased expression of the AMPK-target and transcriptional coactivator PGC1α in Fnip1 null skeletal muscle. Genetic disruption of PGC1α rescued normal levels of type I fiber markers MyH7 and myoglobin in Fnip1-null mice. Remarkably, loss of Fnip1 profoundly mitigated muscle damage in a murine model of Duchenne muscular dystrophy. These results indicate that Fnip1 controls skeletal muscle fiber type specification and warrant further study to determine whether inhibition of Fnip1 has therapeutic potential in muscular dystrophy diseases.

Phototransduction Influences Metabolic Flux and Nucleotide Metabolism in Mouse Retina.

Production of energy in a cell must keep pace with demand. Photoreceptors use ATP to maintain ion gradients in darkness, whereas in light they use it to support phototransduction. Matching production with consumption can be accomplished by coupling production directly to consumption. Alternatively, production can be set by a signal that anticipates demand. In this report we investigate the hypothesis that signaling through phototransduction controls production of energy in mouse retinas. We found that respiration in mouse retinas is not coupled tightly to ATP consumption. By analyzing metabolic flux in mouse retinas, we also found that phototransduction slows metabolic flux through glycolysis and through intermediates of the citric acid cycle. We also evaluated the relative contributions of regulation of the activities of α-ketoglutarate dehydrogenase and the aspartate-glutamate carrier 1. In addition, a comprehensive analysis of the retinal metabolome showed that phototransduction also influences steady-state concentrations of 5'-GMP, ribose-5-phosphate, ketone bodies, and purines.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls.

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors.

OBJECTIVE: Serous ovarian carcinoma (OC) represents a leading cause of cancer-related death among U.S. women. Non-invasive tools have recently emerged for discriminating benign from malignant ovarian masses, but evaluation remains ongoing, without widespread implementation. In the last decade, metabolomics has matured into a new avenue for cancer biomarker development. Here, we sought to identify novel plasma metabolite biomarkers to distinguish serous ovarian carcinoma and benign serous ovarian tumor. METHODS: Using liquid chromatography-mass spectrometry, we conducted global and targeted metabolite profiling of plasma isolated at the time of surgery from 50 serous OC cases and 50 serous benign controls. RESULTS: Global lipidomics analysis identified 34 metabolites (of 372 assessed) differing significantly (P<0.05) between cases and controls in both training and testing sets, with 17 candidates satisfying FDR q<0.05, and two reaching Bonferroni significance. Targeted profiling of ~150 aqueous metabolites identified a single amino acid, alanine, as differentially abundant (P<0.05). A multivariate classification model built using the top four lipid metabolites achieved an estimated AUC of 0.85 (SD=0.07) based on Monte Carlo cross validation. Evaluation of a hybrid model incorporating both CA125 and lipid metabolites was suggestive of increased classification accuracy (AUC=0.91, SD=0.05) relative to CA125 alone (AUC=0.87, SD=0.07), particularly at high fixed levels of sensitivity, without reaching significance. CONCLUSIONS: Our results provide insight into metabolic changes potentially correlated with the presence of serous OC versus benign ovarian tumor and suggest that plasma metabolites may help differentiate these two conditions.

Targeted metabolic profiling of wounds in diabetic and nondiabetic mice.

While cellular metabolism is known to regulate a number of key biological processes such as cell growth and proliferation, its role in wound healing is unknown. We hypothesized that cutaneous injury would induce significant metabolic changes and that the impaired wound healing seen in diabetes would be associated with a dysfunctional metabolic response to injury. We used a targeted metabolomics approach to characterize the metabolic profile of uninjured skin and full-thickness wounds at day 7 postinjury in nondiabetic (db/-) and diabetic (db/db) mice. By liquid chromatography mass spectrometry, we identified 129 metabolites among all tissue samples. Principal component analysis demonstrated that uninjured skin and wounds have distinct metabolic profiles and that diabetes alters the metabolic profile of both uninjured skin and wounds. Examining individual metabolites, we identified 62 with a significantly altered response to injury in the diabetic mice, with many of these, including glycine, kynurenate, and OH-phenylpyruvate, implicated in wound healing for the first time. Thus, we report the first comprehensive analysis of wound metabolic profiles, and our results highlight the potential for metabolomics to identify novel biomarkers and therapeutic targets for improved wound healing outcomes.

Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring.

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring.

Colorectal cancer detection using targeted serum metabolic profiling.

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.

Selection for early reproduction leads to accelerated aging and extensive metabolic remodeling in Drosophila melanogaster populations.

Experimental evolution studies that feature selection on life-history characters are a proven approach for studying the evolution of aging and variation in rates of senescence. Recently, the incorporation of genomic and transcriptomic approaches into this framework has led to the identification of hundreds of genes associated with different aging patterns. However, our understanding of the specific molecular mechanisms underlying these aging patterns remains limited. Here, we incorporated extensive metabolomic profiling into this framework to generate mechanistic insights into aging patterns in Drosophila melanogaster . Specifically, we characterized metabolomic change over time associated with accelerated aging in populations of D. melanogaster under selection for early reproduction compared to their controls. Using this data we: i) evaluated the evolutionary repeatability across the metabolome; ii) evaluated the value of the metabolome as a predictor of "biological age" in this system; and iii) identified specific metabolic pathways associated with accelerated aging. Generally, our findings suggest that the metabolome is a reliable predictor of age and senescence in populations that share a recent evolutionary history. Metabolomic analysis revealed that generations of selection for early reproduction resulted in highly repeatable alterations to the metabolome. Specifically, changes in carbohydrate, amino acid, and TCA cycle-related metabolite abundances over time point to metabolic remodeling that favors rapid early reproduction with long-term consequences for carbohydrate and protein utilization.

Wide-ranging genetic variation in sensitivity to rapamycin in Drosophila melanogaster.

The progress made in aging research using laboratory organisms is undeniable. Yet, with few exceptions, these studies are conducted in a limited number of isogenic strains. The path from laboratory discoveries to treatment in human populations is complicated by the reality of genetic variation in nature. To model the effect of genetic variation on the action of the drug rapamycin, here we use the growth of Drosophila melanogaster larvae. We screened 140 lines from the Drosophila Genetic References Panel for the extent of developmental delay and found wide-ranging variation in their response, from lines whose development time is nearly doubled by rapamycin, to those that appear to be completely resistant. Sensitivity did not associate with any single genetic marker, nor with any gene. However, variation at the level of genetic pathways was associated with rapamycin sensitivity and might provide insight into sensitivity. In contrast to the genetic analysis, metabolomic analysis showed a strong response of the metabolome to rapamycin, but only among the sensitive larvae. In particular, we found that rapamycin altered levels of amino acids in sensitive larvae, and in a direction strikingly similar to the metabolome response to nutrient deprivation. This work demonstrates the need to evaluate interventions across genetic backgrounds and highlights the potential of omic approaches to reveal biomarkers of drug efficacy and to shed light on mechanisms underlying sensitivity to interventions aimed at increasing lifespan.

Metabolite Predictors of Breast and Colorectal Cancer Risk in the Women's Health Initiative.

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.