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Despite a high degree of homology, insulin receptor (IR) and IGF-1 receptor (IGF1R) mediate distinct cellular and physiological functions. Here, we demonstrate how domain differences between IR and IGF1R contribute to the distinct functions of these receptors using chimeric and site-mutated receptors. Receptors with the intracellular domain of IGF1R show increased activation of Shc and Gab-1 and more potent regulation of genes involved in proliferation, corresponding to their higher mitogenic activity. Conversely, receptors with the intracellular domain of IR display higher IRS-1 phosphorylation, stronger regulation of genes in metabolic pathways and more dramatic glycolytic responses to hormonal stimulation. Strikingly, replacement of leucine973 in the juxtamembrane region of IR to phenylalanine, which is present in IGF1R, mimics many of these signalling and gene expression responses. Overall, we show that the distinct activities of the closely related IR and IGF1R are mediated by their intracellular juxtamembrane region and substrate binding to this region.

Background/Objectives Maternal obesity increases risk for childhood obesity, but molecular mechanisms are not well understood. We hypothesized that primary umbilical vein endothelial cells (HUVEC) from infants of overweight and obese mothers would harbor transcriptional patterns reflecting offspring obesity risk. Subjects/Methods In this observational cohort study, we recruited 13 lean (pre-pregnancy BMI <25.0 kg/m2) and 24 overweight-obese (‘ov-ob’, BMI ≥25.0 kg/m2) women. We isolated primary HUVEC, and analyzed both gene expression (Primeview, Affymetrix) and cord blood levels of hormones and adipokines. Results: 142 transcripts were differentially expressed in HUVEC from infants of overweight-obese mothers (false discovery rate, FDR <0.05). Pathway analysis revealed that genes involved in mitochondrial and lipid metabolism were negatively correlated with maternal BMI (FDR <0.05). To test whether these transcriptomic patterns were associated with distinct nutrient exposures in the setting of maternal obesity, we analyzed the cord blood lipidome and noted significant increases in levels of total free fatty acids (lean: 95.5 ± 37.1 ug/ml, ov-ob: 124.1 ± 46.0 ug/ml, P=0.049), palmitate (lean: 34.5 ± 12.7 ug/ml, ov-ob: 46.3 ± 18.4 ug/ml, P=0.03) and stearate (lean: 20.8 ± 8.2 ug/ml, ov-ob: 29.7 ± 17.2 ug/ml, P=0.04), in infants of overweight-obese mothers. Conclusion: Prenatal exposure to maternal obesity alters HUVEC expression of genes involved in mitochondrial and lipid metabolism, potentially reflecting developmentally-programmed differences in oxidative and lipid metabolism.

Skeletal muscle is a highly regenerative tissue, but muscle repair potential is increasingly compromised with advancing age. In this study, we demonstrate that increased NF-κB activity in aged muscle fibers contributes to diminished myogenic potential of their associated satellite cells. We further examine the impact of genetic modulation of NF-κB signaling in muscle satellite cells or myofibers on recovery after damage. These studies reveal that NF-κB activity in differentiated myofibers is sufficient to drive dysfunction of muscle regenerative cells via cell-non-autonomous mechanisms. Inhibition of NF-κB, or its downstream target Phospholipase A2, in myofibers rescued muscle regenerative potential in aged muscle. Moreover, systemic administration of sodium salicylate, an FDA-approved NF-κB inhibitor, decreased inflammatory gene expression and improved repair in aged muscle. Together, these studies identify a unique NF-κB regulated, non-cell autonomous mechanism by which stem cell function is linked to lipid signaling and homeostasis, and provide important new targets to stimulate muscle repair in aged individuals.

Objective: Plasma levels of branched-chain amino acids (BCAA) are consistently elevated in obesity and type 2 diabetes (T2D) and can also prospectively predict T2D. However, the role of BCAA in the pathogenesis of insulin resistance and T2D remains unclear. Methods: To identify pathways related to insulin resistance, we performed comprehensive gene expression and metabolomics analyses in skeletal muscle from 41 humans with normal glucose tolerance and 11 with T2D across a range of insulin sensitivity (SI, 0.49 to 14.28). We studied both cultured cells and mice heterozygous for the BCAA enzyme methylmalonyl-CoA mutase (Mut) and assessed the effects of altered BCAA flux on lipid and glucose homeostasis. Results: Our data demonstrate perturbed BCAA metabolism and fatty acid oxidation in muscle from insulin resistant humans. Experimental alterations in BCAA flux in cultured cells similarly modulate fatty acid oxidation. Mut heterozygosity in mice alters muscle lipid metabolism in vivo, resulting in increased muscle triglyceride accumulation, increased plasma glucose, hyperinsulinemia, and increased body weight after high-fat feeding. Conclusions: Our data indicate that impaired muscle BCAA catabolism may contribute to the development of insulin resistance by perturbing both amino acid and fatty acid metabolism and suggest that targeting BCAA metabolism may hold promise for prevention or treatment of T2D.

Targeting brown adipose tissue (BAT) content or activity has therapeutic potential for treating obesity and the metabolic syndrome by increasing energy expenditure. Both inter- and intra-individual differences contribute to heterogeneity in human BAT and potentially to differential thermogenic capacity in human populations. Here, we demonstrated the generated clones of brown and white preadipocytes from human neck fat of four individuals and characterized their adipogenic differentiation and thermogenic function. Combining an uncoupling protein 1(UCP1) reporter system and expression profiling, we defined novel sets of gene signatures in human preadipocytes that could predict the thermogenic potential of the cells once they were maturated in culture. Knocking out the positive UCP1 regulators identified by this approach, PREX1 and EDNRB in brown preadipocytes using CRISPR/Cas9 markedly abolished the high level of UCP1 in brown adipocytes differentiated from the preadipocytes. Finally, we were able to prospectively isolate adipose progenitors with great thermogenic potential using cell surface marker CD29. These data provide new insights into the cellular heterogeneity in human fat and offer the identification of possible biomarkers of thermogenically competent preadipocytes.

In Caenorhabditis elegans, ablation of germline stem cells (GSCs) extends lifespan, but also increases fat accumulation and alters lipid metabolism, raising the intriguing question of how these effects might be related. Here, we show that a lack of GSCs results in a broad transcriptional reprogramming in which the conserved detoxification regulator SKN-1/Nrf increases stress resistance, proteasome activity, and longevity. SKN-1 also activates diverse lipid metabolism genes and reduces fat storage, thereby alleviating the increased fat accumulation caused by GSC absence. Surprisingly, SKN-1 is activated by signals from this fat, which appears to derive from unconsumed yolk that was produced for reproduction. We conclude that SKN-1 plays a direct role in maintaining lipid homeostasis in which it is activated by lipids. This SKN-1 function may explain the importance of mammalian Nrf proteins in fatty liver disease and suggest that particular endogenous or dietary lipids might promote health through SKN-1/Nrf. DOI: http://dx.doi.org/10.7554/eLife.07836.001

Objective: Dysregulated muscle metabolism is a cardinal feature of human insulin resistance (IR) and associated diseases, including type 2 diabetes (T2D). However, specific reactions contributing to abnormal energetics and metabolic inflexibility in IR are unknown. Methods: We utilize flux balance computational modeling to develop the first systems-level analysis of IR metabolism in fasted and fed states, and varying nutrient conditions. We systematically perturb the metabolic network to identify reactions that reproduce key features of IR-linked metabolism. Results: While reduced glucose uptake is a major hallmark of IR, model-based reductions in either extracellular glucose availability or uptake do not alter metabolic flexibility, and thus are not sufficient to fully recapitulate IR-linked metabolism. Moreover, experimentally-reduced flux through single reactions does not reproduce key features of IR-linked metabolism. However, dual knockdowns of pyruvate dehydrogenase (PDH), in combination with reduced lipid uptake or lipid/amino acid oxidation (ETFDH), does reduce ATP synthesis, TCA cycle flux, and metabolic flexibility. Experimental validation demonstrates robust impact of dual knockdowns in PDH/ETFDH on cellular energetics and TCA cycle flux in cultured myocytes. Parallel analysis of transcriptomic and metabolomics data in humans with IR and T2D demonstrates downregulation of PDH subunits and upregulation of its inhibitory kinase PDK4, both of which would be predicted to decrease PDH flux, concordant with the model. Conclusions: Our results indicate that complex interactions between multiple biochemical reactions contribute to metabolic perturbations observed in human IR, and that the PDH complex plays a key role in these metabolic phenotypes.

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.

Background: Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. Results: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. Conclusion: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.

Background: Anaplastic astrocytoma (AA) and its more aggressive counterpart, glioblastoma multiforme (GBM), are the most common intrinsic brain tumors in adults and are almost universally fatal. A deeper understanding of the molecular relationship of these tumor types is necessary to derive insights into the diagnosis, prognosis, and treatment of gliomas. Although genomewide profiling of expression levels with microarrays can be used to identify differentially expressed genes between these tumor types, comparative studies so far have resulted in gene lists that show little overlap. Results: To achieve a more accurate and stable list of the differentially expressed genes and pathways between primary GBM and AA, we performed a meta-analysis using publicly available genome-scale mRNA data sets. There were four data sets with sufficiently large sample sizes of both GBMs and AAs, all of which coincidentally used human U133 platforms from Affymetrix, allowing for easier and more precise integration of data. After scoring genes and pathways within each data set, we combined the statistics across studies using the nonparametric rank sum method to identify the features that differentiate GBMs and AAs. We found >900 statistically significant probe sets after correction for multiple testing from the >22,000 tested. We also used the rank sum approach to select >20 significant Biocarta pathways after correction for multiple testing out of >175 pathways examined. The most significant pathway was the hypoxia-inducible factor (HIF) pathway. Our analysis suggests that many of the most statistically significant genes work together in a HIF1A/VEGF-regulated network to increase angiogenesis and invasion in GBM when compared to AA. Conclusion: We have performed a meta-analysis of genome-scale mRNA expression data for 289 human malignant gliomas and have identified a list of >900 probe sets and >20 pathways that are significantly different between GBM and AA. These feature lists could be utilized to aid in diagnosis, prognosis, and grade reduction of high-grade gliomas and to identify genes that were not previously suspected of playing an important role in glioma biology. More generally, this approach suggests that combined analysis of existing data sets can reveal new insights and that the large amount of publicly available cancer data sets should be further utilized in a similar manner.