Caenorhabditis elegans. Sampling several time points from young adulthood until early old age, our study covers the full duration of aging and include transcriptomics, and targeted MS-based metabolomics. In order to focus on the metabolic changes due to age we used two strains that are metabolically close to wild-type, yet are conditionally non-reproductive. Using these data in combination with a whole-genome model of the metabolism of C. elegans and mathematical modeling, we predicted metabolic fluxes during early aging. We find that standard Flux Balance Analysis does not accurately predict in vivo measured fluxes nor age-related changes associated with the Citric Acid cycle. We present a novel Flux Balance Analysis method where we combined biomass production and targeted metabolomics information to generate an objective function that is more suitable for aging studies. We validated this approach with a detailed case study of the age-associated changes in the Citric Acid cycle. Our approach provides a comprehensive time-resolved multi-omics and modeling resource for studying the metabolic changes during normal aging in C. elegans.In this contribution, we describe a multi-omics systems biology study of the metabolic changes that occur during aging in  C. elegans is widely used as a model organism to interrogate the molecular mechanisms of aging  can circumvent this problem because it decreases progeny production by reducing germ cell division, however this treatment has been shown to directly influence metabolism  for in vivo measured fluxes. While ordinarily, the objective function for FBA is linked to growth, for aging post-mitotic multicellular organisms, growth can no longer be assumed to be the appropriate objective. Thus, we create an objective function that qualitatively incorporates in vivo measured metabolomics information by instructing the model that if a metabolite level changes significantly between two time points, the associated reactions should either produce or consume that metabolite. We find that when using the TCA cycle as a case study, the usage of multi-omics data with FBA provides more accurate predictions than standard FBA, indicating that the new objective function is better suited for FBA studies in post-developmental worms. This optimized method correctly predicts that the most decreased fluxes within the TCA metabolic shift are through oxaloacetate, in line with experiments that show that it becomes the most limiting TCA-related metabolite in old animals. The model also provides a mechanistic explanation that links the decreased production of glutamate to a malfunctioning TCA cycle. In summary, we present a valuable multi-omics resource that can be used as a baseline for other early aging studies as well as an improved systems biology tool that provides a closer representation to the in vivo metabolism of normal lived animals.In this study we have used two infertile but normal-lived strains grown in the absence of FUDR, to conduct temporally resolved multi-omics studies, including transcriptomics, and targeted MS-based metabolomics. We characterize the changes over the course of aging in about 100 metabolites to find 44 that change significantly with age. Among these, more than half of them have already been characterized as longevity modulators. The data shows metabolic shifts during the course of aging, with a prominent decrease in the levels of aminoacids while their polyamine derivatives increase. In addition, metabolites involved in the TCA cycle become imbalanced before worms reach middle age. We harnessed this data using FBA during the course of normal aging before midlife. When looking at central carbon metabolism, we noticed that the model constrained by transcriptomics was unable to accurately match previous Additional methods can be found in List of strains used in this study:mgIs49 IVGR1395 MOC001 GR1395 outcrossed to N2, 7 timesgon-2(q388); gem-1(bc364)EJ1171 fem-3(q20) IVJK816 gon-2(q388); gem-1(bc364); mgIs49 IVMOC91 2+ containing plates and thawed regularly. Before using them for experiments they were passed for 2 generations at 16°C in regular media without Mg2+. Conditional sterility was obtained by growing L1 larvae to adulthood at restrictive temperature (25°C). At 25°C the penetrance of gonadogenesis failure in gon-2(q388); gem-1(bc364) is <100%, whereas for fem-3(q20) the penetrance is complete.GR1395 was obtained from A. Frand's lab  to ensure all animals arrested at the L1 stage. This produced a tightly synchronized population which was confirmed by mlt-10p::GFP fluorescence. Experimental populations were placed at 25°C on HT115 E. coli containing the empty vector plasmid L4440 on standard NGM plates containing 50 μg/ml Carbenicillin, 1 mM IPTG, and 10 μg/ml Nystatin until harvesting. For the gon-2(q388); gem-1(bc364); strain, samples were inspected before harvesting and visibly fertile worms were picked off. Plates with large numbers of visibly fertile worms were discarded. (Note that under high magnification a degenerate gonad could be seen in a subset of gon-2(q388); gem-1(bc364); animals at 117 h (D4) and were not discarded). Samples were harvested at the following hours post-feeding of arrested L1:Worms were maintained at 16°C on NGM with OP50 Day 1: Hours 41 and 49 post feeding (3 replicates per strain per time point)Day 2: Hours 65 and 73 post feeding (3 replicates per strain per time point)Day 3: Hours 89 and 97 post feeding (3 replicates per strain per time point)Day 4: 117 h post feeding (3 replicates per strain)Day 5: 137 h post feeding (2 replicates per strain)Day 10: 257 h post feeding (2 replicates per strain).Samples of at least 1,000 worms were prepared as described in Supplementary Methods “RNA-Sequencing Library Preparation.” For PCR library enrichment, 13 cycles of amplification were performed. Library quality was assessed on a Bioanalyzer High Sensitivity DNA Chip  and concentration was determined using KAPA Library Quantification Kit (KK4824). Libraries were sequenced on an Illumina HiSeq 2,500 system by the Babraham Sequencing Facility.C. elegans WBCel235 genome assembly using HISAT2  and all subsequent analysis was performed in R (http://www.R-project.org). The DESeq2 package  from a batch containing ~2,000 worms per replicate. Worms were washed several times with M9 and snap frozen in water. Samples were homogenized with a bullet blender in 0.1x PBS at 4°C, protein precipitated with methanol, sonication, and centrifugation. The supernatant was removed and dried in a vacufuge (Speedvac) at 30°C. All samples were processed in parallel to avoid batch effects.Targeted metabolomics using Mass Spectrometry was performed by the Northwestern Metabolomics Research Center  operated in a HILIC regime at 40 C. Solvent A consisted of 95% water, 3% acetonitrile, 2% methanol, 0.2% Acetic Acid (v/v/v/v) 10 mM ammonium acetate, pH ~4.2. Solvent B consisted of 93% acetonitrile, 5% water, 2% methanol, 0.2% acetic acid, and 10 mM ammonium acetate. Organic solvents and acetic acid were Optima grade from Fisher Scientific USA, ammonium acetate was from Sigma Aldrich. 18.2 MOhm water was from a Synergy UV system by Millipore. Gradient at 0.300 mL/min was as follows: 0–3 min 95% B, 3–8 min 95–50% B, 8–12 min 50% B, 12–13 min 50–95% B 13–18.1 min 95% B. During the injection on columns of opposite polarity solvent continued at 95% B giving each column ~23 min of equilibration time. Samples were normalized to total protein content quantified by Bradford assay.http://www.R-project.org). As is typical in this type of metabolomics data, there were several missing values in the dataset. We removed metabolites that had more than 10% such missing values across all samples, leaving 105 metabolites. For the remainder of the missing values, we interpolated them by replacing missing values with the row (metabolite) mean  so as not to affect downstream analyses. This is a standard practice in metabolomics analyses. The dataset was then transformed using the inverse hyperbolic sine, which is linear for small x while asymptotically approaching log(2x). In particular, zero values are mapped to zero and all other values are mapped to positive values (which is not true for log-transformation). The data were further normalized by mean-centering and scaling so that every metabolite had a mean of 0 and a standard deviation of 1, rendering the values comparable. The normalized dataset can be found in Metabolomics data analysis was performed using R  for each pair of samples x and y. The density plot of sample to sample distances was plotted by categorizing the pairs  of samples according to whether they were replicates or had the same age (but different strains) or had the same strain (but different ages).Principal component analysis (PCA) was conducted for both the transcriptomics and the metabolomics datasets using the “prcomp” package in R and the samples were visualized as distributed in the first two principal components. Sample to sample correlations were calculated and plotted using the “corrplot” package in R. Sample to sample distances were calculated using 1-cor(GSE124994 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgiacc=GSE124994). The metabolomics dataset is available as a Supplementary File (see The RNA-Sequencing dataset is available from the Gene Expression Omnibus (GEO) database with accession To determine which metabolites were significantly changing with age in our metabolomics dataset, partial least squares discriminant analysis (PLS-DA), as implemented in the R package “ropls” . The CobraPy library in Python . The biomass reaction used as the default objective function included glycans, phospholipids, collagens, DNA, RNA, free fatty acids, glycogens, proteins, triacylglycerols, and trehalose. FBA was executed using COBRApy's parsimonious FBA method “cobra.flux_analysis.pfba.”We used the WormJam The transcriptomics data were first log-transformed and then means were obtained for biological replicates in order to have a single expression value per time point and strain. These were integrated with the model using a two-step approach. First, a context-specific model was built by removing reactions mediated by genes whose expression was below a threshold . Accordingly, we added the net-production or net-consumption of changed metabolites as an additional objective function alongside the production of biomass, simultaneously maximizing biomass and production or consumption of the relevant metabolites.There is no direct correspondence between metabolite levels and fluxes in FBA. Previous approaches to integrate metabolomics data with FBA include: (1) requiring that detected metabolites are produced in non-zero quantities by the model  of the N time points; flux predictions at the initial time point are thus unaffected by the metabolomics data. Moreover, there were no significant metabolite level changes between time points 89 and 97 h (day 3). Only metabolites which could be mapped to model metabolites were included: this was the case for 75 of the 105 measured metabolites. Of those, 63 metabolites had at least one significant between-time-point difference, while 12 were unchanged across all time points.For each metabolite, a C. elegans is a hermaphrodite species that produces progeny through self-fertilization, which can contaminate the aging parental samples. To avoid this confounding factor, we used two conditionally sterile strains, gon-2(q388)ts; gem-1(bc364) , and fem-3(q20)ts . The gon-2 gene encodes a cation channel required for division of postembryonic gonadal precursor cells ts mutation is enhanced by mutation of the solute carrier gem-1 ts mutation causes temperature sensitive masculinization of the germline  for metabolic reactions using FBA and this approach has been previously used to predict fluxes in normal and perturbed aging in worms . We first performed flux-based pathway analysis to detect pathways that were significantly changed in day 10 compared to day 5 FEM animals  and several products of the TCA cycle , the enzyme that produces alpha-ketoglutarate from isocitrate   . In addi in vivo , includinock out .in vivo fluxes were measured in wild type (N2) young adult animals lacking isocitrate dehydrogenase activity, and the in silico experiment was performed using a transcriptome obtained from FEM young adult animals, one possibility is that the unmatched genetic backgrounds explain the differential fluxes. We think this is unlikely to be the main reason for the discrepancy, because the Pearson correlation of gene expression values between wild type and FEM animals at day 1 of adulthood is close to 98%, and samples only begin to diverge more significantly after day 2 of adulthood (data not shown). Although it remains a possibility, this in silico experiment suggests that although standard FBA can correctly predict global fluxes in aging animals, when looking at the results at a finer scale, the predictions may not be accurate. One likely scenario is that an objective function geared only toward biomass production may not appropriately reflect the biology of post-mitotic somatic cells in adult aging animals, and that new objective functions are required to study metabolism during aging. To obtain a more accurate objective function, we enhanced our FBA pipeline by integrating a metabolomics dataset into the objective function together with the biomass production . Using a comprehensive search of the literature we found 6 additional metabolites amongst our age-associated metabolites that act as longevity modulators when supplemented , our model may be used to predict system level metabolic shifts or changes in metabolites that are more difficult to quantify .We anticipate that other approaches to changing the objective function will also enhance the use of FBA for the study of aging in aak-2, a master regulator of energy homeostasis . There are also NAD+ synthesis stimulation therapies being marketed for human consumption . Our model also predicts that the TCA cycle can have an impact on the fluxes of amino acids such as glutamate. Other more extensive predictions that relate the TCA cycle to lipid metabolism will be possible when the metabolic reconstruction model becomes extensively annotated for these pathways.Overall, we observe that the use of FBA with metabolomics as part of the objective function provided accurate predictions with regards to the most affected reactions of the TCA cycle with age. Specifically, using the combined objective function accurately predicted that oxaloacetate is the metabolite that becomes most limiting with age, a prediction that matches the observation that it is the single most effective TCA cycle intermediate in extending lifespan upon supplementation (Williams et al., OC and JH designed the study. JH, JP, BV, and OC performed the analyses. AM, BV, and SM performed experiments. BV, SM, and AM arranged the Figures. JH and OC wrote the manuscript and all authors contributed to editing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.