Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics.

Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two -omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient's dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction.

Effect of Hyperglycemia on Gene Expression during Early Organogenesis in Mice.

BACKGROUND: Cardiovascular and neural malformations are common sequels of diabetic pregnancies, but the underlying molecular mechanisms remain unknown. We hypothesized that maternal hyperglycemia would affect the embryos most shortly after the glucose-sensitive time window at embryonic day (ED) 7.5 in mice. METHODS: Mice were made diabetic with streptozotocin, treated with slow-release insulin implants and mated. Pregnancy aggravated hyperglycemia. Gene expression profiles were determined in ED8.5 and ED9.5 embryos from diabetic and control mice using Serial Analysis of Gene Expression and deep sequencing. RESULTS: Maternal hyperglycemia induced differential regulation of 1,024 and 2,148 unique functional genes on ED8.5 and ED9.5, respectively, mostly in downward direction. Pathway analysis showed that ED8.5 embryos suffered mainly from impaired cell proliferation, and ED9.5 embryos from impaired cytoskeletal remodeling and oxidative phosphorylation (all P ≤ E-5). A query of the Mouse Genome Database showed that 20-25% of the differentially expressed genes were caused by cardiovascular and/or neural malformations, if deficient. Despite high glucose levels in embryos with maternal hyperglycemia and a ~150-fold higher rate of ATP production from glycolysis than from oxidative phosphorylation on ED9.5, ATP production from both glycolysis and oxidative phosphorylation was reduced to ~70% of controls, implying a shortage of energy production in hyperglycemic embryos. CONCLUSION: Maternal hyperglycemia suppressed cell proliferation during gastrulation and cytoskeletal remodeling during early organogenesis. 20-25% of the genes that were differentially regulated by hyperglycemia were associated with relevant congenital malformations. Unexpectedly, maternal hyperglycemia also endangered the energy supply of the embryo by suppressing its glycolytic capacity.

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis.

Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters. Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

A central role for Notch in effector CD8(+) T cell differentiation.

Activated CD8(+) T cells choose between terminal effector cell (TEC) or memory precursor cell (MPC) fates. We found that the signaling receptor Notch controls this 'choice'. Notch promoted the differentiation of immediately protective TECs and was correspondingly required for the clearance of acute infection with influenza virus. Notch activated a major portion of the TEC-specific gene-expression program and suppressed the MPC-specific program. Expression of Notch was induced on naive CD8(+) T cells by inflammatory mediators and interleukin 2 (IL-2) via pathways dependent on the metabolic checkpoint kinase mTOR and the transcription factor T-bet. These pathways were subsequently amplified downstream of Notch, creating a positive feedback loop. Notch thus functions as a central hub where information from different sources converges to match effector T cell differentiation to the demands of an infection.